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Evidence of Completion of Milestone 3: Experimental Testbed

Construction and deployment of the experimental testbed encountered scheduling delays, and completion was subsequently further delayed by curtailment of LANL operations due to the COVID-19 pandemic. Between March and October, LANL was in a state of “Limited Operations” such that only certain “mission-critical” work was performed with approximately 25% on-site staffing. In spite of this, much of the lab space preparation was executed and the five initial model house” units were constructed and installed during this period. Still, the pace of construction was impacted by difficulty in coordinating personnel and minimizing contact between workers. Currently, LANL is in the mode of “Normal Operations with Maximum Telework,” which continues to limit the availability of on-site personnel. Nevertheless, Milestone 3 is now complete. This document describes the experiment as it is deployed and provides status for each task for this milestone.

99 GENERAL AND MISCELLANEOUS↗

Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications.

Schranz, Thomas↗

Shark nanobodies with potent SARS-CoV-2 neutralizing activity and broad sarbecovirus reactivity

Despite rapid and ongoing vaccine and therapeutic development, SARS-CoV-2 continues to evolve and evade, presenting a need for next-generation diverse therapeutic modalities. Here we show that nurse sharks immunized with SARS-CoV-2 recombinant receptor binding domain (RBD), RBD-ferritin (RFN), or spike protein ferritin nanoparticle (SpFN) immunogens elicit a set of new antigen receptor antibody (IgNAR) molecules that target two non-overlapping conserved epitopes on the spike RBD. Representative shark antibody variable NAR-Fc chimeras (ShAbs) targeting either of the two epitopes mediate cell-effector functions, with high affinity to all SARS-CoV-2 viral variants of concern, including the divergent Omicron strains. The ShAbs potently cross-neutralize SARS-CoV-2 WA-1, Alpha, Beta, Delta, Omicron BA.1 and BA.5, and SARS-CoV-1 pseudoviruses, and confer protection against SARS-CoV-2 challenge in the K18-hACE2 transgenic mouse model. Structural definition of the RBD-ShAb01-ShAb02 complex enabled design and production of multi-specific nanobodies with enhanced neutralization capacity, and picomolar affinity to divergent sarbecovirus clade 1a, 1b and 2 RBD molecules. These shark nanobodies represent potent immunotherapeutics both for current use, and future sarbecovirus pandemic preparation.

60 APPLIED LIFE SCIENCES↗

Comprehensive characterization of N- and O- glycosylation of SARS-CoV-2 human receptor angiotensin converting enzyme 2

The emergence of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created the need for development of new therapeutic strategies. Understanding the mode of viral attachment, entry and replication has become a key aspect of such interventions. The coronavirus surface features a trimeric spike (S) protein that is essential for viral attachment, entry and membrane fusion. The S protein of SARS-CoV-2 binds to human angiotensin converting enzyme 2 (hACE2) for entry. Herein, we describe glycomic and glycoproteomic analysis of hACE2 expressed in HEK293 cells. We observed high glycan occupancy (73.2 to 100%) at all seven possible N-glycosylation sites and surprisingly detected one novel O-glycosylation site. To deduce the detailed structure of glycan epitopes on hACE2 that may be involved in viral binding, we have characterized the terminal sialic acid linkages, the presence of bisecting GlcNAc and the pattern of N-glycan fucosylation. We have conducted extensive manual interpretation of each glycopeptide and glycan spectrum, in addition to using bioinformatics tools to validate the hACE2 glycosylation. Our elucidation of the site-specific glycosylation and its terminal orientations on the hACE2 receptor, along with the modeling of hACE2 glycosylation sites can aid in understanding the intriguing virus-receptor interactions and assist in the development of novel therapeutics to prevent viral entry. Here, the relevance of studying the role of ACE2 is further increased due to some recent reports about the varying ACE2 dependent complications with regard to age, sex, race and pre-existing conditions of COVID-19 patients.

59 BASIC BIOLOGICAL SCIENCES↗

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Antibodies↗

Salt International Collaborations (FY22 Update)

This report summarizes the international collaborations conducted by Sandia funded by the US Department of Energy Office (DOE) of Nuclear Energy Spent Fuel and Waste Science & Technology (SFWST) as part of the Sandia National Laboratories Salt R&D and Salt International work packages. This report satisfies the level-three milestone M3SF-22SN010303063. Several stand-alone sections make up this summary report, each completed by the participants. The sections discuss international collaborations on geomechanical benchmarking exercises (WEIMOS), granular salt reconsolidation (KOMPASS), engineered barriers (RANGERS), numerical model comparison (DECOVALEX) and an NEA Salt Club working group on the development of scenarios as part of the performance assessment development process. Finally, we summarize events related to the US/German Workshop on Repository Research, Design and Operations. The work summarized in this annual update has occurred during the COVID-19 pandemic, and little international or domestic travel has occurred. Most of the collaborations have been conducted via email or as virtual meetings, but a slow return to travel and in-person meetings has begun.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Machine Learning-driven Molecular Design for Therapeutic Discovery

The ongoing novel coronavirus pandemic (COVID-19) has highlighted the need for new therapeutics to counter the threat of emerging viral pathogens. The main proteases are a promising target for developing antiviral inhibitors. In this work, we utilized a novel combination of artificial intelligence-driven iterative design of covalent inhibitor candidates, physics-based computational modeling of protein-inhibitor interactions, and “All in One” Native MS biophysical assay screening and characterization of therapeutic candidates. With our existing expertise in hit generation using a particular scaffold as a starting point, we first generated tens of thousands of compounds that preserve the key scaffold. In order to optimize the candidates, we calculated about 136 descriptors consisting of 2D and 3D features for molecules targeting the SARS-CoV-2 Main protease (Mpro). These compounds were initially filtered according to properties and further sorted by predicted binding affinity using our automated docking modeling and machine learning methods. We tested a handful of candidates and identified two as inhibitors of Mpro with micromolar affinities.

59 BASIC BIOLOGICAL SCIENCES↗

A Hands-On Curriculum for Training in HPC Cluster Deployment and Management

This paper presents the design, methodology, and outcomes of the High-Performance Computing Technologies (HPCT) course, a hands-on training program focused on the system-side of HPC cluster deployment and administration. Delivered as part of the Master in High Performance Computing (MHPC) program, the course introduces students to key concepts in cluster configuration, including networking, software stack provisioning, job scheduling, and monitoring. Initially taught in person, the course was transitioned to an online format during the COVID-19 pandemic. This shift led to the development of openly available instructional material and a flipped-classroom approach that continues to support both in-person and hybrid delivery. All course materials are publicly available at www.hpc.temple.edu/mhpc/hpc-technology/index.html. By documenting the structure, infrastructure, and evolution of HPCT, this paper offers a model for accessible HPC system training that supports workforce development in computational science.

Posada Correa, Fernando [ORNL] (ORCID:000000022565↗

Assessing the use of portable air cleaners for reducing exposure to airborne diseases in a conference room with thermal stratification

The COVID-19 pandemic has highlighted the need for strategies that mitigate the risk of aerosol disease transmission in indoor environments with different ventilation strategies. It is necessary for building operators to be able to estimate and compare the relative impacts of different mitigation strategies to determine suitable strategies for a particular situation. Using a validated CFD model, this study simulates the dispersion of exhaled contaminants in a thermally stratified conference room with overhead heating. The impacts of portable air-cleaners (PACs) on the room airflow and contaminant distribution were evaluated for different PAC locations and flow rates, as well as for different room setups (socially distanced or fully occupied). To obtain a holistic view of a strategy's impacts under different release scenarios, we simultaneously model the steady-state distribution of aerosolized virus contaminants from eight distinct sources in 18 cases for a total of 144 release scenarios. The simulations show that the location of the source, the PAC settings, and the room set-up can impact the average exposure and PAC effectiveness. For this studied case, the PACs reduced the room average exposure by 31%-66% relative to the baseline case. Some occupant locations were shown to have a higher-than-average exposure, particularly those seated near the airflow outlet, and occupants closest to sources tended to see the highest exposure from said source. We found that these PACs were effective at reducing the stratification caused by overhead heating, and also identified at least one sub-optimal location for placing a PAC in this space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Partner with a Third-Party Delivery Service or Not? A Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Here, our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.

97 MATHEMATICS AND COMPUTING↗

Central Asia Seismic Hazard Assessment (CASHA): A Probabilistic Seismic Hazard Assessment for Kazakhstan, Kyrgyzstan and Tajikistan

Probabilistic seismic hazard assessments (PSHA) underpin the calculation of earthquake loads in most building codes around the world. In Central Asia, the building codes are slowly being updated to incorporate some of the contemporary concepts of seismic hazard representation. There is also a regional desire to coordinate hazard assessments and building code modernization. However, some challenges remain. Expertise in the region related to seismic hazard assessments is still largely compartmentalised, requiring a significant amount of training and capacity building in seismic hazard assessment related topics. In addition, there are vast amounts of seismic data (bulletin and waveforms), both from analogue and digital eras, that the region’s countries stored but until recently did not use or share among themselves or with the broader seismological community around the world. Finally, after the collapse of the Soviet Union in the 1990s, many of the countries’ seismic networks suffered a major setback with the lack of attention and budget to update existing equipment and installation of new instruments. In order to address these issues, the United States Department of Energy through Lawrence Livermore National Laboratory (LLNL) initiated a project in 2016 to engage and train local scientists in Central Asia to install new equipment, to enhance the quality of seismic monitoring and reporting, to improve and harmonise the regional earthquake catalogue, and to conduct national probabilistic seismic hazard assessments using the new and improved datasets. To achieve the seismic hazard assessment related goals, a series of workshops were held in Almaty, Kazakhstan; Bishkek, Kyrgyzstan; and Dushanbe, Tajikistan from 2016 until 2020. During the time that the COVID-19 pandemic restricted travel, workshops continued online (22 online workshops were hosted in two years). Finally, in May 2022, an in-person workshop in Istanbul, Turkey brought together all project participants along with civil engineers engaged with building code activities in their respective countries, providing a platform to discuss the implementation of the hazard models into updates of building codes in each country, as well as to discuss model parameters, sensitivity analyses and model results in terms of hazard maps, uniform hazard spectra and hazard deaggregations. The workshops were a combination of lectures and hands-on exercises, and included international participation as well as local scientists and engineers. The workshops served several purposes, including training, coordination of data collection, interactions between local earth scientists and engineers, and brainstorming and knowledge exchange among local and international experts. This report outlines the new earthquake catalogue compilation effort and the PSHA project undertaken in Kyrgyzstan, Tajikistan, and Kazakhstan as part of this initiative. The southern part of this region is tectonically active with moderate to high levels of both shallow crustal seismic activity and occurrence of deeper earthquakes under the Hindu Kush and Pamir mountain ranges. Deeper earthquakes also occur near southwestern Kazakhstan, under the eastern Greater Caucasus and Caspian Sea. Large portions of central and northern Kazakhstan, on the other hand, are in stable continental regions with low levels of seismic activity. This study systematically compiles and improves all available data on local seismicity, active faults, and ground motion attenuation characteristics of the region; and builds a framework to enable a contemporary PSHA to be carried out with the engagement of local scientists. While the project was regional, the seismic hazard assessments are primarily driven by the countries’ own national preferences and understanding of data collection, interpretation, and validation of results.

58 GEOSCIENCES↗

Biochemical and structural insights into SARS-CoV-2 polyprotein processing by Mpro

SARS-CoV-2, a human coronavirus, is the causative agent of the COVID-19 pandemic. Its genome is translated into two large polyproteins subsequently cleaved by viral papain-like protease and main protease (Mpro). Polyprotein processing is essential yet incompletely understood. We studied Mpro-mediated processing of the nsp7-11 polyprotein, whose mature products include cofactors of the viral replicase, and identified the order of cleavages. Integrative modeling based on mass spectrometry (including hydrogen-deuterium exchange and cross-linking) and x-ray scattering yielded a nsp7-11 structural ensemble, demonstrating shared secondary structural elements with individual nsps. The pattern of cross-links and HDX footprint of the C145A Mpro and nsp7-11 complex demonstrate preferential binding of the enzyme active site to the polyprotein junction sites and additional transient contacts to help orient the enzyme on its substrate for cleavage. Last, proteolysis assays were used to characterize the effect of inhibitors/binders on Mpro processing/inhibition using the nsp7-11 polyprotein as substrate.

60 APPLIED LIFE SCIENCES↗

Estimating the strength of selection for new SARS-CoV-2 variants

Abstract Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present two models for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals that uncertainty is large very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗

Revisiting the guidelines for ending isolation for COVID-19 patients

Since the start of the COVID-19 pandemic, two mainstream guidelines for defining when to end the isolation of SARS-CoV-2-infected individuals have been in use: the one-size-fits-all approach (i.e. patients are isolated for a fixed number of days) and the personalized approach (i.e. based on repeated testing of isolated patients). We use a mathematical framework to model within-host viral dynamics and test different criteria for ending isolation. By considering a fixed time of 10 days since symptom onset as the criterion for ending isolation, we estimated that the risk of releasing an individual who is still infectious is low (0–6.6%). However, this policy entails lengthy unnecessary isolations (4.8–8.3 days). In contrast, by using a personalized strategy, similar low risks can be reached with shorter prolonged isolations. The obtained findings provide a scientific rationale for policies on ending the isolation of SARS-CoV-2-infected individuals.

Jeong, Yong Dam↗

Bay Area Regional Energy: Network Integrated Commercial Retrofits (BRICR) Project. Final Report

The BRICR project applied large-scale building energy modeling concepts with the aim of reducing the cost of energy efficiency targeting, design, and project development, and measurement of energy savings for energy efficiency programs implemented by local governments that serve small and medium commercial buildings (SMB). The project leveraged the services and resources of existing local government energy programs serving disadvantaged and hard-to-reach SMB customers. In contrast to programs run by utilities, local government programs generally do not have direct access to energy billing records for an entire class of customers in a geographic area, which prior research demonstrated useful for large-scale building energy model baseline development and calibration. , However, local governments are rich in public records that offer important clues about physical attributes and uses that, along with behavior, determine energy use. Relying only on public records, BRICR demonstrated development of credible baseline energy models for 3,792 office, retail, and hotel buildings. Publicly disclosed annual energy use data from a local energy benchmarking program and anonymized data from the Building Performance Database, the nation’s largest dataset about energy-related characteristics of buildings, were utilized to validate and calibrate energy models via an innovative method comparing distributions of energy intensity by fuel type for portfolios of buildings of similar size, vintage, and use. Portfolio calibration does not provide certainty that an energy model fits an individual building; the method is useful when billing data is not accessible – a common situation for researchers, energy service providers and ESCOs, local governments, and any party other than a utility. A software component was developed, the BRICR gem, which automates simulation when relevant data is added or edited by the user to a file saved in the standardized BuildingSync XML schema for energy audit data. The component was demonstrated as a simplified means to generate a mass of energy models corresponding to public records containing basic attributes such as building scale, location, use, year built, and aspect ratio in combination with building energy code prototype data corresponding to use and vintage. The component was also demonstrated as a simplified means to automate energy simulation when attributes are revised; the intention was to enable iterative improvement of the baseline model and energy savings estimates for common energy conservation measures as users revise relevant attributes based on their observations. In the context of institutional change and uncertainty for the participating local government energy programs, 13 whole building retrofits were completed. Impacts were measured by applying the CalTRACK2.0 methods to standardize measurement of normalized metered energy consumption. The GRIDMeter methods of stratified sampling and individual load shape analysis were applied to adjust for impacts of the effect of COVID-19 on retrofitted buildings in the context of all local buildings of similar size and use. Excluding impacts of the pandemic, retrofitted buildings demonstrated between 1.6% and 25.1% reduction in energy use. The project contributed use cases and feedback that helped inform evolution of the software tools and data formats that were combined for the first time in the BRICR project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Interspecies Transmission from Pigs to Ferrets of Antigenically Distinct Swine H1 Influenza A Viruses with Reduced Reactivity to Candidate Vaccine Virus Antisera as Measures of Relative Zoonotic Risk

During the last decade, endemic swine H1 influenza A viruses (IAV) from six different genetic clades of the hemagglutinin gene caused zoonotic infections in humans. The majority of zoonotic events with swine IAV were restricted to a single case with no subsequent transmission. However, repeated introduction of human-seasonal H1N1, continual reassortment between endemic swine IAV, and subsequent drift in the swine host resulted in highly diverse swine IAV with human-origin genes that may become a risk to the human population. To prepare for the potential of a future swine-origin IAV pandemic in humans, public health laboratories selected candidate vaccine viruses (CVV) for use as vaccine seed strains. To assess the pandemic risk of contemporary US swine H1N1 or H1N2 strains, we quantified the genetic diversity of swine H1 HA genes, and identified representative strains from each circulating clade. We then characterized the representative swine IAV against human seasonal vaccine and CVV strains using ferret antisera in hemagglutination inhibition assays (HI). HI assays revealed that 1A.3.3.2 (pdm09) and 1B.2.1 (delta-2) demonstrated strong cross reactivity to human seasonal vaccines or CVVs. However, swine IAV from three clades that represent more than 50% of the detected swine IAVs in the USA showed significant reduction in cross-reactivity compared to the closest CVV virus: 1A.1.1.3 (alpha-deletion), 1A.3.3.3-clade 3 (gamma), and 1B.2.2.1 (delta-1a). Representative viruses from these three clades were further characterized in a pig-to-ferret transmission model and shown to exhibit variable transmission efficiency. Our data prioritize specific genotypes of swine H1N1 and H1N2 to further investigate in the risk they pose to the human population.

60 APPLIED LIFE SCIENCES↗

Benchtop assessment of sealing efficacy and breathability of additively manufactured (AM) face masks

The onset of the 2019 novel coronavirus disease (COVID-19) led to a shortage of personal protective equipment (PPE), medical devices, and other medical supplies causing many stakeholders and the general public alike to turn to additive manufacturing (AM) as a stopgap when normally accessible devices were not available. However, without a method to test these AM constructs, there continued to be a disconnect between AM suppliers and the community’s needs. The objective of this study was to characterize the pressure drop and leakage of four different publicly available AM face mask models with two filter material combinations, as well as to investigate the impact of frame modification techniques including the use of foam strips and hot-water face forming to improve fit when the masks are donned on manikin head forms. AM face mask frame designs were downloaded from public repositories during the early stages of the COVID-19 pandemic. AM face masks were fabricated and tested on manikin head forms within a custom chamber containing dry aerosolized NaCl. Pressure drops, particle penetration, and leakage were evaluated for various flow rates and NaCl concentrations. Results indicated that filter material combination and frame modification played a major role in the overall performance of the AM face masks studied. Filter material combinations showed improved performance when high filtration fabric was used, and the cross-sectional area of the fabric was increased. AM frame modifications appeared to improve AM face mask leakage performance by as much as 69.6%.

36 MATERIALS SCIENCE↗

Physiological Characterization of Language Comprehension

In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.

42 ENGINEERING↗