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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Polymer, Additives, and Processing Effects on N95 Filter Performance

The current severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) pandemic has highlighted the need for personal protective equipment, specifically filtering facepiece respirators like N95 masks. While it is common knowledge that polypropylene (PP) is the industry standard material for filtration media, trial and error is often required to identify suitable commercial precursors for filtration media production. This work aims to identify differences between several commercial grades of PP and demonstrate the development of N95 filtration media with the intent that the industry partners can pivot and help address N95 shortages. Three commercial grades of high melt flow index PP were melt blown at Oak Ridge National Laboratory and broadly characterized by several methods including differential scanning calorimetry (DSC), X-ray diffraction (XRD), and neutron scattering. Despite the apparent similarities (high melt flow and isotacticity) between PP feedstocks, the application of corona charging and charge enhancing additives improve each material to widely varying degrees. From the analysis performed here, the most differentiating factor appears to be related to crystallization of the polymer and the resulting electret formation. Materials with higher crystallization onset temperatures, slower crystallization rates, and larger number of crystallites form a stronger electret and are more effective at filtration.

36 MATERIALS SCIENCE↗
DOE OSTI2024

1T'-transition metal dichalcogenide monolayers stabilized on 4H-Au nanowires for ultrasensitive SERS detection

Unconventional 1T'-phase transition metal dichalcogenides (TMDs) have aroused tremendous research interest due to their unique phase-dependent physicochemical properties and applications. However, due to the metastable nature of 1T'-TMDs, the controlled synthesis of 1T'-TMD monolayers (MLs) with high phase purity and stability still remains a challenge. Here we report that 4H-Au nanowires (NWs), when used as templates, can induce the quasi-epitaxial growth of high-phase-purity and stable 1T'-TMD MLs, including WS 2 , WSe 2 , MoS 2 and MoSe 2 , via a facile and rapid wet-chemical method. The as-synthesized 4H-Au@1T'-TMD core–shell NWs can be used for ultrasensitive surface-enhanced Raman scattering (SERS) detection. For instance, the 4H-Au@1T'-WS 2 NWs have achieved attomole-level SERS detections of Rhodamine 6G and a variety of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. Finally, this work provides insights into the preparation of high-phase-purity and stable 1T'-TMD MLs on metal substrates or templates, showing great potential in various promising applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗
DOE OSTI2020

Antimicrobial Silver Touch Surfaces in an Occupational Medicine Clinic

Global efforts are being marshalled against the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. A key component of public health response is sanitizing or disinfecting the workplace, including the healthcare environment. Recently we launched what appears to be the first study of anti-microbial properties of silver hardware touch surfaces or fomites in an occupational health clinic setting. Here, we preview our major finding in order to emphasize the vital role of active sanitation measures, as opposed to reliance on passive controls, in public health response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal knowledge graph with a causal question and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. Here, we demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm and performing counterfactual inference to predict the causal effect of medical countermeasures for severely ill COVID-19 patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Resolving Lipid Structures for Threat-Agnostic Signatures

Development of threat-agnostic biosignatures is critical for the detection of and response to biological threats that go beyond the historical list-based approach. Lipids are a class of structurally diverse biomolecules that hold great promise as relevant threat-agnostic biosignatures, yet the fundamental challenge of resolving and decoding structural differences among lipids remains an obstacle in the pursuit of signature development. Structural analysis of complex lipids remains stymied due to spectral complexity and the inability to resolve low abundance lipids. To address these challenges, we integrated ozonolysis with ion mobility mass spectrometry (IMS-MS) analysis to determine the structural information for complex lipid species and (2) reduced lipid sample complexity prior to introduction into the IMS-MS using normal phase high pressure liquid chromatography (HPLC) to enable the ozonolysis of low abundance lipids. We applied these new capabilities to define the lipid structures in antimicrobial resistant (AMR) and antimicrobial susceptible (AMS) biothreat organism Yersinia pestis and cells infected with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) variants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Antiviral and clinical activity of bamlanivimab in a randomized trial of non-hospitalized adults with COVID-19

Abstract Anti-SARS-CoV-2 monoclonal antibodies are mainstay COVID-19 therapeutics. Safety, antiviral, and clinical efficacy of bamlanivimab were evaluated in the randomized controlled trial ACTIV-2/A5401. Non-hospitalized adults were randomized 1:1 within 10 days of COVID-19 symptoms to bamlanivimab or blinded-placebo in two dose-cohorts (7000 mg, n = 94; 700 mg, n = 223). No differences in bamlanivimab vs placebo were observed in the primary outcomes: proportion with undetectable nasopharyngeal SARS-CoV-2 RNA at days 3, 7, 14, 21, and 28 (risk ratio = 0.82–1.05 for 7000 mg [ p (overall) = 0.88] and 0.81–1.21 for 700 mg [ p (overall) = 0.49]), time to symptom improvement (median 21 vs 18.5 days [ p = 0.97], 7000 mg; 24 vs 20.5 days [ p = 0.08], 700 mg), or grade 3+ adverse events. However, bamlanivimab was associated with lower day 3 nasopharyngeal viral levels and faster reductions in inflammatory markers and viral decay by modeling. This study provides evidence of faster reductions in nasopharyngeal SARS-CoV-2 RNA levels but not shorter symptom durations in non-hospitalized adults with early variants of SARS-CoV-2. Trial Registration: ClinicalTrials.gov Identifier: NCT04518410.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Immunogenicity and protective efficacy of a rhesus adenoviral vaccine targeting conserved COVID-19 replication transcription complex

The COVID-19 pandemic marks the third coronavirus pandemic this century (SARS-CoV-1, MERS, SARS-CoV-2), emphasizing the need to identify and evaluate conserved immunogens for a pan-sarbecovirus vaccine. Here we investigate the potential utility of a T-cell vaccine strategy targeting conserved regions of the sarbecovirus proteome. We identified the most conserved regions of the sarbecovirus proteome as portions of the RNA-dependent RNA polymerase (RdRp) and Helicase proteins, both of which are part of the coronavirus replication transcription complex (RTC). Fitness constraints suggest that as SARS-CoV-2 continues to evolve these regions may better preserve cross-reactive potential of T-cell responses than Spike, Nucleocapsid, or Membrane proteins. We sought to determine if vaccine-elicited T-cell responses to the highly conserved regions of the RTC would reduce viral loads following challenge with SARS-CoV-2 in mice using a rhesus adenovirus serotype 52 (RhAd52) vector. The RhAd52.CoV.Consv vaccine generated robust cellular immunity in mice and led to significant reductions in viral loads in the nasal turbinates following challenge with a mouse-adapted SARS-CoV-2. These data suggest the potential utility of T-cell targeting of conserved regions for a pan-sarbecovirus vaccine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Host genetics and gut microbiota composition: Baseline gut microbiota composition as a possible prognostic factor for the severity of COVID-19 in patients with familial Mediterranean fever disease

It is known that the gut microbiome of a healthy person affects the process of COVID-19 after getting infected with SARS-CoV-2 virus. It is also believed that colchicine can alleviate the severity of COVID-19. Current investigations aimed to evaluate the associations between the baseline gut microbiota composition of healthy and Familial Mediterranean fever (FMF) - carrier Armenian men populations, and the severity of the COVID-19 disease after their infection with the SARS-CoV-2. The study has a purpose of answering three core questions: i. Do the characteristics of gut microbiome of Armenians affect the course of COVID-19 severity? ii. How does the COVID-19 disease course on go for FMF patients who have been taking colchicine as a medication over the years after getting infected with SARS-CoV-2? iii. Is there an initial gut micribiota structure pattern for non-FMF and FMF patients in the cases when COVID-19 appears in mild form? The gut microbiota composition in non-FMF and FMF patients before the first infection (mild and moderate course of COVID-19) was considered. COVID-19 was diagnosed by SARS-CoV-2 nucleic acid RT-PCR in nasopharyngeal swab and/or sputum. The number of patients with male FMF with mild COVID-19 was approximately two times higher than that of non-FMF male subjects with COVID-19. In addition, an association of COVID-19 disease severity with the baseline gut Prevotella, Clostridium hiranonis, Eubacterium biforme, Veillonellaceae, Coprococcus, and Blautia diversities in the non-FMF and FMF populations were revealed by us, which can be used as risk/prognostic factor for the severity of COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Trifan, Anda↗
DOE OSTI2020

Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index

Background Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. Methods and findings We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality–no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77–0.81), and in early (AUC = 0.81 95% CI: 0.78–0.83) and late (AUC = 0.84, 95% CI: 0.78–0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60–64 years, overall mortality was estimated at 9% (95% CI: 6–11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3–7%) to 21% (95% CI: 12–31%), depending on sex and comorbid disease. Conclusion Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

3C-like protease inhibitors block coronavirus replication in vitro and improve survival in MERS-CoV–infected mice

Pathogenic coronaviruses are a major threat to global public health, as exemplified by severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and the newly emerged SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19). We describe herein the structure-guided optimization of a series of inhibitors of the coronavirus 3C-like protease (3CLpro), an enzyme essential for viral replication. The optimized compounds were effective against several human coronaviruses including MERS-CoV, SARS-CoV, and SARS-CoV-2 in an enzyme assay and in cell-based assays using Huh-7 and Vero E6 cell lines. Two selected compounds showed antiviral effects against SARS-CoV-2 in cultured primary human airway epithelial cells. In a mouse model of MERS-CoV infection, administration of a lead compound 1 day after virus infection increased survival from 0 to 100% and reduced lung viral titers and lung histopathology. These results suggest that this series of compounds has the potential to be developed further as antiviral drugs against human coronaviruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of a pharmacological approach to reduce ACE2 expression and development of an in vitro COVID-19 viral entry model

Because of rapid emergence and circulation of the SARS-CoV-2 variants, especially Omicron which shows increased transmissibility and resistant to antibodies, there is an urgent need to develop novel therapeutic drugs to treat COVID-19. In this study we developed an in vitro cellular model to explore the regulation of ACE2 expression and its correlation with ACE2-mediated viral entry. We examined ACE2 expression in a variety of human cell lines, some of which are commonly used to study SARS-CoV-2. Using the developed model, we identified a number of inhibitors which reduced ACE2 protein expression. The greatest reduction of ACE2 expression was observed when CK869, an inhibitor of the actin-related protein 2/3 (ARP2/3) complex, was combined with 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), an inhibitor of sodium-hydrogen exchangers (NHEs), after treatment for 24 h. Using pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein, we found that ACE2-dependent viral entry was inhibited in CK869 + EIPA-treated Calu-3 and MDA-MB-468 cells. This study provides an in vitro model that can be used for the screening of novel therapeutic candidates that may be warranted for further pre-clinical and clinical studies on COVID-19 countermeasures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Diverse array of neutralizing antibodies elicited upon Spike Ferritin Nanoparticle vaccination in rhesus macaques

The repeat emergence of SARS-CoV-2 variants of concern (VoC) with decreased susceptibility to vaccine-elicited antibodies highlights the need to develop next-generation vaccine candidates that confer broad protection. Here we describe the antibody response induced by the SARS-CoV-2 Spike Ferritin Nanoparticle (SpFN) vaccine candidate adjuvanted with the Army Liposomal Formulation including QS21 (ALFQ) in non-human primates. By isolating and characterizing several monoclonal antibodies directed against the Spike Receptor Binding Domain (RBD), N-Terminal Domain (NTD), or the S2 Domain, we define the molecular recognition of vaccine-elicited cross-reactive monoclonal antibodies (mAbs) elicited by SpFN. We identify six neutralizing antibodies with broad sarbecovirus cross-reactivity that recapitulate serum polyclonal antibody responses. In particular, RBD mAb WRAIR-5001 binds to the conserved cryptic region with high affinity to sarbecovirus clades 1 and 2, including Omicron variants, while mAb WRAIR-5021 offers complete protection from B.1.617.2 (Delta) in a murine challenge study. Our data further highlight the ability of SpFN vaccination to stimulate cross-reactive B cells targeting conserved regions of the Spike with activity against SARS CoV-1 and SARS-CoV-2 variants.

59 BASIC BIOLOGICAL SCIENCES↗