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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Observational constraints on early dark energy

In this paper, we review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 and PR4 cosmic microwave background (CMB) data and large-scale structure (LSS) data sets including galaxy clustering and weak lensing data from the Dark Energy Survey, Subaru Hyper Suprime-Cam and KiDS+VIKING-450, as well as BOSS/eBOSS galaxy clustering and Lyman-[Formula: see text] forest data. We detail the fit to CMB data, and perform the first analyses of EDE using the CAMSPEC and Hillipop likelihoods for Planck CMB data, rather than Plik, both of which yield a tighter upper bound on the allowed EDE fraction than that found with Plik. We then supplement CMB data with LSS data in a series of new analyses. All these analyses are concordant in their Bayesian preference for [Formula: see text]CDM over EDE, as indicated by marginalized posterior distributions. We perform a series of tests of the impact of priors in these results, and compare with frequentist analyses based on the profile likelihood, finding qualitative agreement with the Bayesian results. All these tests suggest prior volume effects are not a determining factor in analyses of EDE. This work provides both a review of existing constraints and several new analyses.

Astronomy & Astrophysics↗
DOE OSTI2023

Single-Cell Discovery and Multiomic Characterization of Therapeutic Targets in Multiple Myeloma

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

An evaluation of GPT models for phenotype concept recognition

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation. The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations. The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches. Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

Abstract Background Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. Methods We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. Results We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations inTP53,PLXNB3, Zinc finger genes, and fewer mutations inSDC2,CDH1,PIK3CA,SVIL, andPTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA–protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, includingKIAA1522,DCK,FOXO3,MYO9B,ARID1A,EPRS,ZC3HAV1, andRBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. Conclusions This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Oncology↗
DOE OSTI2021

Modeling the Metabolic Costs of Heavy Military Backpacking

Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter. This study aimed to develop and validate an equation for estimating metabolic rates during heavy backpacking for the US Army Load Carriage Decision Aid (LCDA), an integrated software mission planning tool. Thirty healthy, active military-age adults (3 women, 27 men; age, 25 ± 7 yr; height, 1.74 ± 0.07 m; body mass, 77 ± 15 kg) walked for 6–21 min while carrying backpacks loaded up to 66% body mass at speeds between 0.45 and 1.97 m·s -1 . A new predictive model, the LCDA backpacking equation, was developed on metabolic rate data calculated from indirect calorimetry. Model estimation performance was evaluated internally by k-fold cross-validation and externally against seven historical reference data sets. We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured metabolic rate. Estimation accuracy and level of agreement were also evaluated by the bias and concordance correlation coefficient (CCC), respectively. Estimates from the LCDA backpacking equation were statistically equivalent ( P < 0.01) to metabolic rates measured in the current study (bias, -0.01 ± 0.62 W·kg -1 ; CCC, 0.965) and from the seven independent data sets (bias, -0.08 ± 0.59 W·kg -1 ; CCC, 0.926). The newly derived LCDA backpacking equation provides close estimates of steady-state metabolic energy expenditure during heavy load carriage. These advances enable further optimization of thermal-work strain monitoring, sports nutrition, and hydration strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metabolic Costs of Walking with Weighted Vests

ABSTRACT Introduction The US Army Load Carriage Decision Aid (LCDA) metabolic model is used by militaries across the globe and is intended to predict physiological responses, specifically metabolic costs, in a wide range of dismounted warfighter operations. However, the LCDA has yet to be adapted for vest-borne load carriage, which is commonplace in tactical populations, and differs in energetic costs to backpacking and other forms of load carriage. Purpose The purpose of this study is to develop and validate a metabolic model term that accurately estimates the effect of weighted vest loads on standing and walking metabolic rate for military mission-planning and general applications. Methods Twenty healthy, physically active military-age adults (4 women, 16 men; age, 26 ± 8 yr old; height, 1.74 ± 0.09 m; body mass, 81 ± 16 kg) walked for 6 to 21 min with four levels of weighted vest loading (0 to 66% body mass) at up to 11 treadmill speeds (0.45 to 1.97 m·s −1 ). Using indirect calorimetry measurements, we derived a new model term for estimating metabolic rate when carrying vest-borne loads. Model estimates were evaluated internally byk-fold cross-validation and externally against 12 reference datasets (264 total participants). We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured walking metabolic rate. Estimation accuracy, precision, and level of agreement were also evaluated by the bias, standard deviation of paired differences, and concordance correlation coefficient (CCC), respectively. Results Metabolic rate estimates using the new weighted vest term were statistically equivalent (P< 0.01) to measured values in the current study (bias, −0.01 ± 0.54 W·kg −1 ; CCC, 0.973) as well as from the 12 reference datasets (bias, −0.16 ± 0.59 W·kg −1 ; CCC, 0.963). Conclusions The updated LCDA metabolic model calculates accurate predictions of metabolic rate when carrying heavy backpack and vest-borne loads. Tactical populations and recreational athletes that train with weighted vests can confidently use the simplified LCDA metabolic calculator provided as Supplemental Digital Content to estimate metabolic rates for work/rest guidance, training periodization, and nutritional interventions.

Sport Sciences↗
DOE OSTI2022

Evaluation of co-circulating pathogens and microbiome from COVID-19 infections

Co-infections or secondary infections with SARS-CoV-2 have the potential to affect disease severity and morbidity. Additionally, the potential influence of the nasal microbiome on COVID-19 illness is not well understood. In this study, we analyzed 203 residual samples, originally submitted for SARS-CoV-2 testing, for the presence of viral, bacterial, and fungal pathogens and non-pathogens using a comprehensive microarray technology, the Lawrence Livermore Microbial Detection Array (LLMDA). Eighty-seven percent of the samples were nasopharyngeal samples, and 23% of the samples were oral, nasal and oral pharyngeal swabs. We conducted bioinformatics analyses to examine differences in microbial populations of these samples, as a proxy for the nasal and oral microbiome, from SARS-CoV-2 positive and negative specimens. We found 91% concordance with the LLMDA relative to a diagnostic RT-qPCR assay for detection of SARS-CoV-2. Sixteen percent of all the samples (32/203) revealed the presence of an opportunistic bacterial or frank viral pathogen with the potential to cause co-infections. The two most detected bacteria, Streptococcus pyogenes and Streptococcus pneumoniae , were present in both SARS-CoV-2 positive and negative samples. Human metapneumovirus was the most prevalent viral pathogen in the SARS-CoV-2 negative samples. Sequence analysis of 16S rRNA was also conducted to evaluate bacterial diversity and confirm LLMDA results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Detectability of Varied Hybridization Scenarios Using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This phenomenon has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, as well as summary methods that predict individual hybridization events from a subset of taxa. However, a lack of empirical comparisons between methods – especially those pertaining to large networks with varied hybridization scenarios – hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson’s D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and HyDe, Patterson’s D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events between sets of three taxa. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow (γ). We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher relative false negative rates across all methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving extinct or unsampled ghost lineages, HyDe is the only method able to identify the direction of hybridization, distinguishing the source parental lineages from recipient hybrid lineages. Lastly, we test the methods on a dataset of ultraconserved elements from the bee subfamily Nomiinae, finding possible hybridization events between clades which correspond to regions of poor support in the species tree estimated in a previous study.

Bjorner, Marianne B.↗
DOE OSTI2023

Opiate Sensitivity in Fruit Flies

Substance use disorder is a debilitating clinical condition in which behavioral dependence results from biological, environmental, genetic, and psychosocial factors. An epidemic surrounding the use and abuse of opioids is ravaging the world. While considerable efforts have explored the social drivers of addiction, a deeper understanding of biological causes and genetic vulnerabilities, preventative interventions, and effective treatments, have all proven elusive. This perspective article aims to remind readers that addictive natural compounds such as cocaine, nicotine, cathinone, or morphine, evolved as defensive metabolites to deter insect herbivory. The molecular mechanisms underlying motivational seeking and learning/reward show remarkable conservation since their early emergence in bilateral metazoans. An extended coevolutionary arms race subsequently weaponized these compounds into disruptors of learning, motivation, and incentivized attention. When plant chemical defenses attack insect physiology, humans are rendered susceptible due to strong conservation in the underlying molecular machinery. This perspective addresses the paradox that opiates were shaped to target insect neuropharmacology, even though this taxon appears to lack the recognized opioid receptor clade of mammals. We argue that the link is to be found in the allatostatin receptor, a basal ortholog of opioid receptors. Moreover, preliminary evidence indicates that morphine reduces Drosophila feeding and locomotion, concordant with a purported role as a defensive compound reducing herbivory. An implementation via allatostatin-mediated mechanisms is likely. This research argues for a broader heuristic perspective of substance abuse and a recognition of the evolutionary constraints that have likely shaped the biological drivers of opioid sensitivity and of its behavioral targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Evaluation of a Smoke Opacity Sensor for Improving Residential Wood Heater Combustion

Residential wood heating lacks real-time particulate matter (PM) monitoring technologies capable of measuring emissions directly from flue gases. This study evaluates the performance of the Octoring, an opacity-based smoke sensor developed to monitor wood heater emissions and provide feedback for improved combustion. The device was tested on catalytic and non-catalytic cordwood heaters and a pellet heater during startup, high-burn, and low-burn phases. Performance was assessed against reference particle instruments and gravimetric measurements using concordance, correlation, and agreement analyses. Results show that temperature-dependent drift is the primary limitation affecting sensor accuracy, with systematic drift observed across ambient and combustion conditions. Agreement with reference instruments ranged from poor to good depending on operating conditions, with the strongest performance observed during pellet heater high-burn operation. These findings establish a performance baseline for opacity-based sensing technologies and identify key areas for further development before practical deployment in residential wood combustion monitoring systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Preliminary Study of Potential Utilization of Triassic Rift Basins for Long-Term Carbon Sequestration

Triassic rift basins of the eastern United States present a potential option for long–term carbon dioxide (CO 2 ) sequestration. Exposed and buried basins are located near large point CO 2 sources. Because of their similar origins, comparable fill successions indicate a level of reproducibility that can be conveyed between basins. The thick rock successions of the exposed Culpeper and Gettysburg basins served as proxies for understanding of the basin sequences. Their study allowed recognition of five mappable assemblages of rock types, herein termed lithofacies associations. These lithofacies associations were formed by alluvial fan, braided and meandering streams and marginal and distal lake depositional processes.Stratigraphic architecture of the lithofacies associations within exposed basins suggests a vertical succession that consists of an initial coarse-grained fluvial succession that is progressively replaced upward and basinward by finer grained lacustrine deposits. Along the faulted margins of the basins alluvial fans transitioning to fluvial delta deposits reflect lateral progradation and contemporaneous lateral components of the basin filling.To test the model devised in exposed basins, facies associations were developed for the buried Taylorsville Basin. This basin preserves over 8,000 feet of Triassic rocks that are concealed beneath more than 2,000 feet of Cretaceous and Tertiary Coastal Plain sediments. Composition and stratigraphic architecture of these rocks are similar to those observed in exposed basins. Infilling of the basin was the result of vertical aggradation and lateral progradation of coarse-grained to fine-grained facies. Based upon study of outcrops in the Culpeper and Gettysburg basins, groups of recurring lithologic facies were identified as the fundamental constructs of the basin’s sediment infilling. These groups of facies, termed lithofacies associations, represent an amalgamation of lithologic components from broadly similar depositional systems. These lithologic associations were then extrapolated into the exposed and buried portions of the Taylorsville basins. This effort determined that Triassic rift basins provide several avenues for potential study of geologic sequestration of carbon. The characteristic rift basin succession in the basins presents possible reservoir targets within the marginal fluvial deposits. Furthermore, intrusive and extrusive mafic bodies present a potential source of fracture porosity that could serve as CO 2 reservoirs. Porosity values from thin section analysis of exposed basin samples are higher in alluvial fan and braided fluvial lithofacies. Porosity is highest in samples that show less compaction, due to a lack of ductile lithic fragments and/or higher stratigraphic position. Log data from the Taylorsville Basin indicates that porosity and permeability values in basin marginal fluvial strata are elevated. Near the center of the basin, porosity and permeability values are greatly reduced, primarily owing to the fine-grained character of the lacustrine deposits. Some Triassic basins also contain thick intervals of concordant extrusive and intrusive mafic igneous rocks. These igneous bodies are potential CO 2 reservoirs for several reasons. Firstly, extrusive lava flows provide potential storage in the layers of primary porosity that occur in the vesicules formed at the top of lava flows. Secondly, both lava flows and subsurface igneous sills exhibit extensive fracture porosity produced by the rapid cooling of the flows and intrusions. Thirdly, these igneous rocks are mafic in composition, and studies have shown that iron- and magnesium-rich mafic rocks provide sequestration opportunities through carbonate remineralization. Lastly, extrusive and intrusive igneous rocks are invariably preserved within fine-grained lake deposits. These lacustrine sediments can serve as a fine-grained confining layer that encases the igneous rocks both above and below.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Nuclear Data to Reduce Uncertainties in Reactor Antineutrino Measurements

The large quantities of antineutrinos produced through the decay of fission fragments in nuclear reactors provide an opportunity to study the properties of these particles and investigate their use in reactor monitoring. The reactor antineutrino spectra are measured using specialized, large area detectors that detect antineutrinos through inverse beta decay, electron elastic scattering, or coherent elastic neutrino nucleus scattering; although, inverse beta decay is the only demonstrated method so far. Reactor monitoring takes advantage of the differences in the antineutrino yield and spectra resulting from uranium and plutonium fission providing an opportunity to estimate the fissile material composition in the reactor. Recent experiments reveal a deviation between the measured and calculated antineutrino flux and spectra (the reactor anomaly) indicating either the existence of yet undiscovered neutrino physics, uncertainties in the reactor source term calculation, incorrect nuclear data, or a combination of all three. To address the nuclear data that impact the antineutrino spectrum calculations and measurements, an international group of over 180 experts in antineutrino physics, reactor analysis, detector development, and nuclear data came together during the Workshop on Nuclear Data for Reactor Antineutrino Measurements (WoNDRAM) to discuss nuclear data needs and achieve concordance on a set of recommended priorities for nuclear data improvements. Three topical sessions focused on the reactor source term, the antineutrino spectrum, and the detector response, provided a forum to gain consensus amongst the participants on the most important data improvements to address two goals: 1) understand the reactor anomaly and 2) improve the ability to monitor reactors using antineutrinos. This report summarizes the outcomes of the workshop discussions and the recommendations for nuclear data efforts that reduce reactor antineutrino measurement uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

NRAP Recommended Practices for Containment Assurance and Leakage Risk Quantification

This report presents recommendations for quantitatively assessing and managing leakage risk and effective containment assurance at geologic carbon storage (GCS) sites, amidst uncertainty in site characteristics and performance. These recommended practices are a product of the U.S. Department of Energy’s (DOE) Office of Fossil Energy and Carbon Management’s National Risk Assessment Partnership (NRAP)—a multi-year collaborative research effort of Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, National Energy Technology Laboratory, and Pacific Northwest National Laboratory. These recommended practices are organized as a set of conceptual workflows that correspond with the stages of the GCS project lifecycle. They consider aspects of site characterization, quantification of potential leakage, assessing the spatial extent of potential environmental impacts, optimizing monitoring design for detection of potential leakage, evaluation of concordance between monitored and forecasted reservoir response, assessing conformance with expected/accepted performance, and determination of the timeline over which risk of impactful leakage can be expected to abate. These conceptual workflows are intended to aid stakeholders in making risk management decisions at GCS sites and to support development of sound, risk-based justifications for characterizing the subsurface, defining area of potential leakage impact, designing site monitoring plans, selecting leakage risk management/mitigation alternatives, and estimating site closure timelines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and Code for Understanding Generative AI Content with Embedding Models

This repository contains code for the experiments in the paper "Understanding Generative AI Content with Embedding Models". Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).

Vargas, Max [Pacific Northwest National Laboratory↗
DOE OSTI2021

Mitochondrial Genomes of the United States Distribution of Gray Fox (Urocyon cinereoargenteus) Reveal a Major Phylogeographic Break at the Great Plains Suture Zone

We examined phylogeographic structure in gray fox (Urocyon cinereoargenteus) across the United States to identify the location of secondary contact zone(s) between eastern and western lineages and investigate the possibility of additional cryptic intraspecific divergences. We generated and analyzed complete mitochondrial genome sequence data from 75 samples and partial control region mitochondrial DNA sequences from 378 samples to investigate levels of genetic diversity and structure through population- and individual-based analyses including estimates of divergence (FST and SAMOVA), median joining networks, and phylogenies. We used complete mitochondrial genomes to infer phylogenetic relationships and date divergence times of major lineages of Urocyon in the United States. Despite broad-scale sampling, we did not recover additional major lineages of Urocyon within the United States, but identified a deep east-west split (~0.8 million years) with secondary contact at the Great Plains Suture Zone and confirmed the Channel Island fox (Urocyon littoralis) is nested within U. cinereoargenteus. Genetic diversity declined at northern latitudes in the eastern United States, a pattern concordant with post-glacial recolonization and range expansion. Beyond the east-west divergence, morphologically-based subspecies did not form monophyletic groups, though unique haplotypes were often geographically limited. Gray foxes in the United States displayed a deep, cryptic divergence suggesting taxonomic revision is needed. Secondary contact at a common phylogeographic break, the Great Plains Suture Zone, where environmental variables show a sharp cline, suggests ongoing evolutionary processes may reinforce this divergence. Follow-up study with nuclear markers should investigate whether hybridization is occurring along the suture zone and characterize contemporary population structure to help identify conservation units. Comparative work on other wide-ranging carnivores in the region should test whether similar evolutionary patterns and processes are occurring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2

Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening

Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history. We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments. In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations. The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

60 APPLIED LIFE SCIENCES↗