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At least 289 records · Page 16

COVID-19, An Exercise in Data Governance at Sandia National Laboratories

In April of 2020, Sandia National Laboratories had an urgent need to identify and manage the data that could be used to create mobile applications, models, reports, and visualizations to assist management in safely bringing the workforce onsite during the COVID-19 pandemic. Multiple divisions volunteered to design and build software solutions; meanwhile, requests for new data sources, including duplicate requests, were inundating Information Technology (IT) and data owners. The Enterprise Data Governance Team was assigned to resolve obtaining and accessing new sources of data in an accelerated timeframe. Through successful collaboration with multiple stakeholders and domain owners across Sandia, the Enterprise Data Governance Team rapidly developed a centralized data strategy and solution for use in safeguarding the Sandia workforce during the COVID-19 pandemic. This foundation enabled teams to successfully develop solutions, including reports for executives and management as well as the data for modeling and scientific analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

The Vault National Security Then & Now (2021 Annual)

Looking back, I can say with certainty that we rose to the challenge. Like other entities all around the globe, the National Security Research Center was tested by the COVID-19 pandemic. As a young organization (we celebrated our second anniversary in June 2021) and one that is critical to Los Alamos National Laboratory’s national security charge, we faced the difficult task of continuing our mission-essential work while keeping our staff safe. The NSRC is one of the largest libraries in the United States, and it houses the country’s most-comprehensive collections of nuclear weapons-related national security material. The NSRC contains millions of documents, films, photos, and other materials on the development, testing, and production of nuclear weapons. These are accessed on a daily basis by scientists, engineers, and researchers at Los Alamos and other National Nuclear Security Administration’s labs and sites, as well as partners in the Department of Defense. It was not an option for us to close our doors - our nation’s security depended on us not to. Not only did we continue operating under new safety protocols, we successfully accomplished many pre-pandemic goals. Thanks to the NSRC’s dedicated staff, our accomplishments during our second year are too numerous to list, though two in particular come to mind.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

A targeted opsonization platform for programming innate immunity against rapidly evolving novel viruses.

Recent work has shown that artificial opsonins stimulate the targeted destruction of bacteria by phagocyte immune cells. Artificial opsonization has the potential to direct the innate immune system to target novel antigens, potentially even viral pathogens. Furthermore, the engagement of innate immunity presents a potential solution for the spread of pandemics in a scenario when a vaccine is unavailable or ineffective. Funded by the LDRD late start bioscience pandemic response program, we tested whether artificial opsonins can be developed to target viral pathogens using phage MS2 and a SARS-CoV-2 surrogate. To direct opsonization against these viruses we purified antibody derived viral targeting motifs and attempted the same chemical conjugation strategies that produced bacterial targeting artificial opsonins. However, the viral targeting motifs proved challenging to conjugate using these methods, frequently resulting in precipitation and loss of product. Future studies may be successful with this approach if a smaller and more soluble viral-targeting peptide could be used.

60 APPLIED LIFE SCIENCES↗

2021 Annual Report: Atmospheric Radiation Measurement

In fiscal year 2021 (FY2021), the COVID-19 pandemic continued to have an impact on activities within the Atmospheric Radiation Measurement (ARM) user facility. Travel was limited, which affected field activities and forced the continuation of virtual meetings. However, the ability to travel expanded significantly through the year, bringing some return to normalcy, and throughout the year, there was a great deal of activity dedicated to advancing the facility. Because of COVID, ARM twice delayed the TRacking Aerosol Convection interactions ExpeRiment (TRACER) in the Houston, Texas, area. Originally planned to launch in the spring, TRACER started October 1, 2021, one month after the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign began near Crested Butte, Colorado. ARM teams worked onsite and remotely to make sure both campaigns could launch on this new schedule that was set early in the year. Data from SAIL and TRACER will be critical to improving earth system models, each contributing to different sets of issues. SAIL measurements will provide insights into how precipitation forms and water travels through the Upper Colorado River Basin. TRACER scientists want to know whether tiny atmospheric particles can influence the severity of thunderstorms. In October 2020, the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition ended after 13 months. MOSAiC data from ARM and other organizations are already helping scientists better understand how ice, atmosphere, and ocean systems are connected in the central Arctic. This report discusses some early investigations from MOSAiC, along with other ARM campaigns and activities generating prolific research. In this report, you will also learn how research activities moved forward during the pandemic at ARM’s fixed-location atmospheric observatories. There were important science applications from measurements across the facility. ARM spent a significant amount of FY2021 looking back—and ahead. In November 2020, ARM had its Triennial Review. This review is held every three years to evaluate ARM’s effectiveness in science, operations, and management. Overall, the reviewers had positive feedback regarding the breadth and impact of science activities using ARM data and the way ARM strives to meet changing user needs. The reviewers also provided some recommendations for strengthening ARM going forward. Incorporating feedback from the review, we finalized an updated Decadal Vision document that will help guide ARM priorities in the coming years. This report describes the four themes driving ARM’s Decadal Vision.

54 ENVIRONMENTAL SCIENCES↗

Biocompatible Electrostatic Layered Systems for Viral Elimination in the Nose/Throat

An anti-viral coating for the nose & throat is needed to quickly address the spread of COVID-19 infections and to aid future pandemics. Current nasal delivery systems are typically a 1-spray homogenous solution, which is convenient but may not be as efficient or durable as a multi-spray solution that contains a binding layer to anchor the anti-viral components. Many of the current nasal solutions being investigated to aid in pandemic response have solution-based mechanisms of action and are quickly cleared from the nose/mouth limiting their efficacy lifetime. A multi-spray approach utilizing strong intermolecular forces between polymeric materials and anti-viral agents to provide a robust biocompatible coating is expected to have increased physical and chemical properties to combat viral infection. This work evaluated which tailored biocompatible materials are needed to generate a layered system to combat viral infection. This layered system takes advantage of electrostatic interactions to bind anti-viral components, shown in Figure 1. Proof of concept success was demonstrated through cell toxicity studies and anti-viral assays on both the individual layer components and the complete layered system. Project work began with chemical modifications of Xantham Gum to hydrolyzing xanthan gum to give a negatively charged polymer with varying degrees of ionic character and investigate bonding efficiencies. However, this was abandoned in favor of comparing COTS available materials listed in Table 1 (Results section). Concentration and adhesion studies were performed on layers created using the listed polymeric materials and anti-viral agents. Finally, biocompatibility of materials, layers, and coating system were confirmed through cytotoxicity studies and the efficacy of the anti-viral properties tested with phi6 bacteriophage as a SARS-CoV2 surrogate.

36 MATERIALS SCIENCE↗

2020 Hydrogen and Fuel Cell Technologies Market Report

This report features summaries of relevant hydrogen and fuel cell activity in 2020, consistent with prior annual reports across applications and sectors. However, 2020 was no ordinary year. Despite pandemic hardships, the hydrogen and fuel cell industry not only stepped up to assist with humanitarian and healthcare efforts but saw continued momentum in several growing market sectors with numerous partnerships, initiatives, and investments. Using hydrogen and fuel cell technologies to provide an environmental and economic pathway to clean, reliable power was a key feature of world economic recovery and energy infrastructure plans. Today, hydrogen energy and fuel cell systems are being developed and deployed in a range of stationary power, portable power, and transportation applications around the world. Throughout 2020, there were significant achievements in each sector, building on years of research and development (R&D), field testing, large-scale demonstration projects, and real-world customer experience. The heavy-duty truck market in particular continued to make substantial gains, with new players entering the space and a coalition of companies pledging to produce and deploy more than 100,000 fuel cell trucks in the next two decades. To provide hydrogen for trucks and other end users, larger and larger electrolyzer projects were announced throughout the year. Collectively, there were more than 430 megawatts (MW) of electrolyzer systems either deployed, ordered, or proposed around the world. This 2020 report also provides special “Spotlights” on the collective industry response to the COVID-19 pandemic as well as international commitments and hydrogen strategies, providing overviews of significant activity during and after the initial onset of COVID-19 around the world.

08 HYDROGEN↗

SARS-CoV-2 transmission control in California correctional facilities

LBNL provided technical assistance to the CalPROTECT project, which is a collaboration between the University of California, Berkeley and the University of California, San Francisco. The CalPORTECT team provided support and guidance to the California Department of Corrections and Rehabilitation (CDCR) and the California Correctional Health Care Services (CCHCS) in their response to the COVID-19 pandemic. The CalPROTECT team provided recommendations to mitigate the risk posed to the residents and staff who live and work in CDCR prisons through considering scientific knowledge of the virus, analyzing anonymized resident and staff level data from CCHCS, evaluation of statewide and institution-level policies, and data collection during site visits to CDCR prisons during the pandemic.

59 BASIC BIOLOGICAL SCIENCES↗

Durable Super-Repellant Materials for Stretchable and Flexible Personal Protective Equipment

The goal of this report is to summarize findings from development of new flexible protective coatings with superhydrophobic properties. For many practical applications, the durability of such coatings must far exceed what it currently possible. We aimed to develop coatings that can be integrated into personal protective equipment to provide an additional barrier that can shed and reduce the spread of infectious liquids. The COVID-19 pandemic has revealed the importance of PPE for frontline healthcare workers. Developing durable protective coatings could have both immediate impacts during the pandemic and long-term benefits in multiple industries.

36 MATERIALS SCIENCE↗

The 38 th Annual Interdisciplinary Plant Group Symposium: Enhancing the Resilience of Plant Systems to Climate Change (Meeting Summary)

The University of Missouri’s Interdisciplinary Plant Group held its 38th annual symposium on May 25–27. In short, it was a resounding success! The air was almost electric because of a confluence of events that were particular to this symposium. Not only was there great enthusiasm from students attending their first ever scientific meeting, but this was coupled with excitement and delight exuded by many of our senior invited speakers, who were attending their first in-person meeting since the Covid pandemic began. After a long planning period, and with the ongoing pandemic injecting uncertainty, it was gratifying to see the symposium unfold with such excitement and engagement among participants. The symposium was organized to address the grand challenge of “Enhancing the Resilience of Plant System to Climate Change”, by bringing together experts at the intersections of multiple disciplines to educate our community on the opportunities to develop innovative mitigation strategies and to adapt plants to climate change.

54 ENVIRONMENTAL SCIENCES↗

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection, was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability." More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture." Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multiscale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator.

59 BASIC BIOLOGICAL SCIENCES↗

Computational and Experimental Investigation of Thermal-Mechanical- Chemical Mechanisms of High-burnup Spent Nuclear Fuel (SNF) Processes at Elevated Temperatures and Degradation Behavior in Geologic Repositories

The overarching goal of the combined computational and experimental R&D activities proposed in this project is to enhance understanding of the mechanisms and thermal-mechanical-chemical (TMC) parameters controlling the instant release fraction (IRF) and matrix dissolution of high-burnup (HB; burnup) spent nuclear fuels (SNFs) and the subsequent formation, stability, and phase transformations of SNF alteration products under long-term storage and geological disposal conditions. Uranium dioxide may undergo oxidative corrosion/alteration, and the IRF may be increased for HB SNF, both of which may affect environmental systems associated with SNF long-term storage and disposal. The oxidative matrix dissolution may form various complex uranyl-based phases, including a rich variety of oxides, silicates, carbonates and other secondary minerals in varied geological environments (e.g., studtite, metastudtite, amorphous uranyl peroxide, uranium trioxide, triuranium octoxide, schoepite, dehydrated schoepite, metaschoepite, becquerelite, soddyite, rutherfordine,...). These uranyl phases generally have higher mobility UO 2 +2 species than less soluble U 4+ phases. However, limited information on the thermodynamic properties and formation kinetics of these uranyl-bearing phases is available to predict explicitly paragenesis under the conditions relevant to long-term storage or disposal. The proposed project draws on complementary expertise and research backgrounds from the team members: (i) to apply a combined ab initio modeling (UNLV/UTEP and SNL) and experimental (UNLV) strategy investigating the high-temperature TMC mechanisms of alteration of SNF under α-radiolysis conditions; (ii) to investigate the mechanistic of phase transformations in UNF degradation products under various conditions expected in long-term storage systems (e.g. (UO 2 )O 2 (H 2 O) 4 → (UO 2 )O 2 (H 2 O) 2 → U 2 O 7 → UO 3 → U 3 O 8 ); (iii) to determine high-accuracy TMC parameters for complex uranyl-based phases formed in storage or geological disposal environments (e.g. UO 3 (H 2 O) 2 , Ca[(UO 2 ) 6 O 4 (OH) 8 ] 8 H 2 O, (UO 2 ) 2 (SiO 4 ) 3 2H 2 O,…). The unforeseen COVID-19 pandemic led to the laboratory/campus closure since March 2020, that resulted in a significant delay in reaching milestones in a satisfactory manner, due to (i) the statewide recommendation from stop-working to later limited work in the lab and work-from-home (WFH), (ii) no in-person interactions, and (iii) a hiring freeze at UNLV. Therefore, a no cost extension (10/01/2021- 9/30/2022) was requested to help make up the time we lost during the global pandemic in 2020-2021, leading to paradigm shifts in the focus of the project in the following three main tasks: Task 1 (Computational), Task 2 (Experimental), and Task 3 (Final report, due on 12/29/2022).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Western States Building Energy & Controls Apprenticeship (BECA) Program

How does this project help to understand the challenges of workforce development in the commercial building energy management industry? The purpose of this project was to create a replicable, scalable, and portable apprenticeship program for building energy management and controls. This paper will demonstrate how local processes can be more adaptable and inclusive than federal processes in achieving workforce development goals. Our work provides insights into the technical effectiveness of the program, enabling others to achieve greater success in their workforce development initiatives. The project required more time and financial resources than initially anticipated and spent a year in a no-cost extension working to accomplish the Statement of Project Objectives. The original goal was to create an Industry Related Apprenticeship Program (IRAP). The development of this apprenticeship presented challenges due to the lack of existing programs for reference and the absence of relevant industry classification codes by the Department of Labor (DOL). Notably, the role of a commercial building energy analyst is not recognized by the DOL. The SIC (industry codes) do not have a good description of this job. There are many that may fall into related categories, but none are the actual duties of an energy analyst. Discussions with the DOL indicated that substantial groundwork was necessary before a national apprenticeship program could be implemented, causing delays in the program’s commencement. As a result, students experienced longer wait times before starting their apprenticeship component. The apprenticeship program officially launched on September 14, 2021. The success of the State of Oregon’s apprenticeship program, the first of its kind in the state, underscored the flexibility and effectiveness of local initiatives compared to federal efforts. The COVID-19 pandemic also significantly impacted the project’s success. Beginning in March 2020, the pandemic led to widespread closures of schools and colleges by fall 2020. By September 2021, when the apprenticeship option became available, enrollment in colleges and universities nationwide had decreased, affecting student participation in the program. Moreover, as employers transitioned their employees to remote work, there was limited interaction with external personnel, influencing the willingness of training agents (employers) to integrate additional workers into their teams. This paper addresses several challenges encountered during the project, with the hope that future workforce development efforts will benefit from these experiences. We Final Technical Report 4 | Page encourage others to engage with state and federal agencies to enhance and update pathways for workforce development and apprenticeship programs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Lessons Learned from the Ongoing Response to the COVID-19 Crisis

The global COVID-19 pandemic has disrupted nearly every facet of daily life and forced state regulators, utilities, industry stakeholders, and customers to adjust to a rapidly changing landscape. At the initial onset of the pandemic, similar to other natural disasters, the immediate focus of response was on safety and life- saving measures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Joint Analysis of the Epidemic Evolution and Human Mobility During the First Wave of COVID-19 in Spain: Retrospective Study

Background The initial wave of the COVID-19 pandemic placed a tremendous strain on health care systems worldwide. To mitigate the spread of the virus, many countries implemented stringent nonpharmaceutical interventions (NPIs), which significantly altered human behavior both before and after their enactment. Despite these efforts, a precise assessment of the impact and efficacy of these NPIs, as well as the extent of human behavioral changes, remained elusive. Objective In this study, we conducted a retrospective analysis of the initial wave of COVID-19 in Spain to better comprehend the influence of NPIs and their interaction with human behavior. Such investigations are vital for devising future mitigation strategies to combat COVID-19 and enhance epidemic preparedness more broadly. Methods We used a combination of national and regional retrospective analyses of pandemic incidence alongside large-scale mobility data to assess the impact and timing of government-implemented NPIs in combating COVID-19. Additionally, we compared these findings with a model-based inference of hospitalizations and fatalities. This model-based approach enabled us to construct counterfactual scenarios that gauged the consequences of delayed initiation of epidemic response measures. Results Our analysis demonstrated that the pre–national lockdown epidemic response, encompassing regional measures and heightened individual awareness, significantly contributed to reducing the disease burden in Spain. The mobility data indicated that people adjusted their behavior in response to the regional epidemiological situation before the nationwide lockdown was implemented. Counterfactual scenarios suggested that without this early epidemic response, there would have been an estimated 45,400 (95% CI 37,400-58,000) fatalities and 182,600 (95% CI 150,400-233,800) hospitalizations compared to the reported figures of 27,800 fatalities and 107,600 hospitalizations, respectively. Conclusions Our findings underscore the significance of self-implemented prevention measures by the population and regional NPIs before the national lockdown in Spain. The study also emphasizes the necessity for prompt and precise data quantification prior to enacting enforced measures. This highlights the critical interplay between NPIs, epidemic progression, and human behavior. This interdependence presents a challenge in predicting the impact of NPIs before they are implemented.

Public, Environmental & Occupational Health↗

Best Practices for NERSC Training

The National Energy Research Supercomputing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL) organizes approximately 20 training events per year for its 8,000 users from 800 projects, who have varying levels of High Performance Computing (HPC) knowledge and familiarity with NERSC's HPC resources. Due to the novel circumstances of the pandemic, NERSC began transforming our traditional smaller-scale, on-site training events to larger-scale, fully virtual sessions in March 2020. We treated this as an opportunity to try new approaches and improve our training best practices. This paper describes the key practices we have developed since the start of this transformation, including considerations for organizing events; collaboration with other HPC centers and the DOE ECP Program to increase reach and impact of events; targeted emails to users to increase attendance; efficient management of user accounts for computational resource access; strategies for preventing Zoombombing; streamlining the publication of professional-quality, closed-captioned videos on the NERSC YouTube channel for accessibility; effective communication channels for Q&A; tailoring training contents to NERSC user needs via close collaboration with vendors and presenters; standardized training procedures and publishing of training materials; and considerations for planning HPC training topics. Additionally, most of these practices will be continued after the pandemic as effective norms for training.

97 MATHEMATICS AND COMPUTING↗

Editorial: Nanotechnology for natural products

Humans around the globe have lived through an unprecedented time. The emergence and rapid spread of coronavirus disease 2019 (COVID-19) have resulted in the loss of millions of lives, and this number is still climbing every day and hour. As a consequence, there have been drastic changes over the past two years in how we live, communicate, and work, some of which may have changed forever: remote conferencing has become a new norm for businesses, schools, and academia; work-from-home and hybrid working is now more than just acceptable in many industries; most importantly, COVID-19 has raised global awareness of infectious diseases and pandemic responses to an exceptional level. Certainly, COVID-19 is not the only disease that is causing human suffering at this moment. Other highly contagious and lethal viruses, such as the human immunodeficiency virus (HIV), Ebola, and the recent addition to the list, Monkeypox, pose extraordinary challenges to modern civilizations. On the other hand, non-communicable diseases, such as heart disease, stroke, and cancer, are still claiming the majority of deaths on a global level (World Health Organization, 2020). Before the availability of synthetic drugs, civilizations worldwide relied on natural products extracted from the environment and various organisms for healthcare. Natural products and their derivatives continue to be critical components in the drug library available today. Indeed, almost a quarter of the new drugs approved by the US Food and Drug Administration (FDA) since 1981 are natural products, botanical mixtures, or their derivatives (Newman and Cragg, 2020). Recent developments in analytical characterization, genome mining, and engineering have further increased interest in drug discovery from the pool of natural products (Atanasov et al., 2021). This Research Topic celebrates a collection of studies at the intersection of natural product research and nanotechnological advances. As a successor and extension of the Research Topic in “Nanotechnology in Traditional Medicines and Natural Products” published last year (Zhang et al., 2021), the present collection focused more on recent developments in employing the synthesis and characterization of nanostructures for the delivery of functional natural products. Specifically, the use of carrier nanostructures to conjugate or encapsulate therapeutically active natural products with carrier nanostructures could enable their robust, targeted delivery tailored to either tackle a specific disease or unlock a drug administration pathway. Over the last decade, nanostructures such as liposomes (Pattni et al., 2015) and lipid nanoparticles (Hou et al., 2021) have been developed as versatile platforms for the inclusion and delivery of a wide range of therapeutical compounds; a significant advance in this aspect is the stabilization of mRNA strands for COVID-19 vaccination recently produced by Pfizer/BioNTech and Moderna (Tenchov et al., 2021). Such engineered nanomaterials are now widely used in the development of new drugs, while regulatory forces, such as the US FDA, remain cautious with the evaluation of each product on a case-by-case basis (Wang and Grainger, 2022). In this collection, Zeng et al. provided a timely review of natural products conjugated to and contained within nanomaterials for the ongoing COVID-19 pandemic. Natural products that are potentially useful in the mitigation of this disease, such as chloroquine and curcumin, supported in nanocarrier systems, have been discussed in detail. More broadly, Cheng et al. comprehensively reviewed the use of liposomes as a nanocarrier for natural products. This article provides a summary of natural product compounds and extracts used to form liposomal structures. Readers can further enjoy the practical aspects of these engineered drugs for the treatment of specific diseases as well as insights into future directions in this field.

peptides↗