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DOE OSTI2025

Understanding the selectivity of nonsteroidal anti-inflammatory drugs for cyclooxygenases using quantum crystallography and electrostatic interaction energy

Quantum crystallography methods have been employed to investigate complex formation between nonsteroidal anti-inflammatory drugs (NSAIDs) and cyclooxygenase (COX) enzymes, with particular focus on the COX-1 and COX-2 isoforms. This study analyzed the electrostatic interaction energies of selected NSAIDs (flurbiprofen, ibuprofen, meloxicam and celecoxib) with the active sites of COX-1 and COX-2, revealing significant differences in binding profiles. Flurbiprofen exhibited the strongest interactions with both COX-1 and COX-2, indicating its potent binding affinity. Celecoxib and meloxicam showed a preference for COX-2, consistent with their known selectivity for this isoform, while ibuprofen showed comparable interaction energies with both isoforms, reflecting its nonselective inhibition pattern. Key amino-acid residues, including Arg120, Arg/His513 and Tyr355, were identified as critical determinants of NSAID selectivity and binding affinity. The findings highlight the complex interplay between interaction energy and selectivity, suggesting that while electrostatic interactions play a fundamental role, additional factors such as enzyme dynamics and the hydrophobic effect also contribute to the therapeutic efficacy and safety profiles of NSAIDs. These insights provide valuable guidance for the rational design of NSAIDs with enhanced therapeutic benefits and minimized adverse effects.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Analysis and Mitigation of Cascading Failures Using a Stochastic Interaction Graph with Eigen-analysis

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. Here, the validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche

Abstract Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.

Microbiology↗

DOE OSTI2026

Progress toward double-differential cross-section measurements of single charged-pion production in charged current muon neutrino interactions on argon with SBND

Resonant neutrino–nucleus processes constitute a significant portion of neutrino interactions in the few-GeV energy region. Charged-current interactions with a muon and one single charged pion in the final state (CC1$\pi$) are primarily sensitive to resonant processes, while also receiving contributions from non-resonant processes and being strongly affected by nuclear effects and final-state interactions. A precise understanding of this channel is essential for improving neutrino interaction models and reducing systematic uncertainties in oscillation measurements. The Short-Baseline Near Detector (SBND) at Fermilab has collected the largest neutrino–argon dataset to date, providing an excellent opportunity to study CC1$\pi$ interactions on argon. This analysis aims to improve existing single-differential cross-section measurements by reporting results in a variety of kinematic variables and to perform the first double-differential CC1$\pi$ cross-section measurement on argon, using novel techniques for pion energy reconstruction.

Pelegrina-Gutiérrez, Luis [Granada U.] (ORCID:0000↗

DOE OSTI2023

Four Years of Type Ia Supernovae Observed by TESS: Early-time Light-curve Shapes and Constraints on Companion Interaction Models

Abstract We present 307 type Ia supernova (SN) light curves from the first 4 yr of the Transiting Exoplanet Survey Satellite mission. We use this sample to characterize the shapes of the early-time light curves, measure the rise times from first light to peak, and search for companion star interactions. Using simulations, we show that light curves must have noise <10% of the peak flux to avoid biases in the early-time light-curve shape, restricting our quantitative analysis to 74 light curves. We find that the mean power-law index t β 1 of the early-time light curves isβ 1 = 1.93 ± 0.57, and the mean rise time to peak is 15.7 ± 3.5 days. The underlying population distribution forβ 1 may instead consist of a Gaussian component with mean 2.29, width 0.34, and a long tail extending to values less than 1.0. We find that the data can rarely distinguish between models with and without companion interaction models. Nevertheless, we find three high-quality light curves that tentatively prefer the addition of a companion interaction model, but the statistical evidence for the companion interactions is not robust. We also find two SNe that disfavor the addition of a companion interaction model to a curved power-law model. Taking the 74 SNe together, we calculate 3σupper limits on the presence of companion signatures to control for orientation effects that can hide companions in individual light curves. Our results rule out common progenitor systems with companions having Roche lobe radii >31R ⊙ (separations >5.7 × 10 12 cm, 99.9% confidence level) and disfavor companions having Roche lobe radii >10R ⊙ (separations >1.9 × 10 12 cm, 95% confidence level). Lastly, we discuss the implications of our results for the intrinsic fraction of single degenerate progenitor systems.

Astronomy & Astrophysics↗

DOE OSTI2024

The Landscape of Thermal Transients from Supernovae Interacting with a Circumstellar Medium

The interaction of supernova ejecta with a surrounding circumstellar medium (CSM) generates a strong shock, which can convert ejecta kinetic energy into observable radiation. Given the diversity of potential CSM structures (arising from diverse mass-loss processes such as late-stage stellar outbursts, binary interaction, and winds), the resulting transients can display a wide range of light-curve morphologies. We provide a framework for classifying the transients arising from interaction with a spherical CSM shell. The light curves are decomposed into five consecutive phases, starting from the onset of interaction and extending through shock breakout and subsequent shock cooling. The relative prominence of each phase in the light curve is determined by two dimensionless quantities representing the CSM-to-ejecta mass ratio η, and the breakout parameter ξ. These two parameters define four light-curve morphology classes, where each class is characterized by the location of the shock breakout and the degree of deceleration as the shock sweeps up the CSM. We compile analytic scaling relations connecting the luminosity and duration of each light-curve phase to the physical parameters. We then run a grid of radiation hydrodynamics simulations for a wide range of ejecta and CSM parameters to numerically explore the landscape of interaction light curves, and to calibrate and confirm the analytic scalings. We connect our theoretical framework to several case studies of observed transients, highlighting the relevance in explaining slow-rising and superluminous supernovae, fast blue optical transients, and double-peaked light curves.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Velocity-space Origins of the Pressure–Strain Interaction in Multipopulation Distributions and Its Application to Magnetic Reconnection

A forefront research question is how energy evolves in weakly collisional plasmas for which departures from local thermodynamic equilibrium (LTE) are significant. The standard approach is studying the terms in the non-LTE energy evolution equation derived by taking the second moment of the Boltzmann equation, but the resultant fluid metrics do not retain information about which particles at which velocities drive energy evolution. A widely studied channel for internal energy density evolution is the pressure–strain interaction. Here, we employ the kinetic pressure–strain, a phase-space diagnostic whose velocity-space integral recovers the pressure–strain interaction to disambiguate the contributions to the pressure–strain interaction from disparate particle populations in composite phase-space densities. We develop phase-space analogs of the pressure–strain interaction decompositions to provide the phase-space origins of normal versus sheared flow. We introduce the “kinetic strain-rate” tensor, the phase-space analog of the strain-rate tensor, which we argue is needed to interpret the phase-space origins of the pressure–strain interaction. To demonstrate the utility of these quantities, we investigate them for composite electron distributions near the electron diffusion region in two-dimensional particle-in-cell simulations of antiparallel symmetric magnetic reconnection. We find that the phase-space-based diagnostics isolate the roles of distinct populations. These results contribute to a growing body of work providing new methods for quantifying phase-space energy evolution for a broad array of processes, from magnetic reconnection to collisionless shocks and turbulence, opening new pathways for answering longstanding problems of particle energization in weakly collisional plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)

Abstract. Large-scale interaction between the three tropical ocean basins is an area of intense research that is often conducted through experimentation with numerical models. A common problem is that modeling groups use different experimental setups, which makes it difficult to compare results and delineate the role of model biases from differences in experimental setups. To address this issue, an experimental protocol for examining interaction between the tropical basins is introduced. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) consists of experiments in which sea surface temperatures (SSTs) are prescribed to follow observed values in selected basins. There are two types of experiments. One type, called standard pacemaker, consists of simulations in which SSTs are restored to observations in selected basins during a historical simulation. The other type, called pacemaker hindcast, consists of seasonal hindcast simulations in which SSTs are restored to observations during 12-month forecast periods. TBIMIP is coordinated by the Climate and Ocean – Variability, Predictability, and Change (CLIVAR) Research Focus on Tropical Basin Interaction. The datasets from the model simulations will be made available to the community to facilitate and stimulate research on tropical basin interaction and its role in seasonal-to-decadal variability and climate change.

Richter, Ingo↗

DOE OSTI2024

Effects of Interactions Between Produced Formation Fluid and Rock Matrix on Pore Structure of Caney Shale, Southern Oklahoma

ABSTRACT: Rock-fluid interactions change properties of shales during exploitation. To investigate effects of rock-fluid interactions on pore structure of shales matrix after hydraulic fracturing, powder samples from two late Mississippian Caney Shale cores in the Ardmore Basin, southern Oklahoma, were used to react with formation produced fluid from the field in the batch reactor analysis. X-ray diffraction for mineralogy and Low-pressure nitrogen adsorption isotherms for pore structure were measured for original, after-7days, and after-30days samples. Results show that the samples consist mainly of quartz, followed by clay minerals, carbonates, and feldspar. The pore sizes of micropore (<2 nm) and mesopore (2-50 nm) increase 14%-233% due to dissolution of pyrite, feldspar, and carbonates after 7 days. Due to the transformation from smectite to illite and the increase of pore size, the specific surface area (SSA) decreases after 7-days interactions. After 30-days interactions, the micropore volume slightly increases and the mesopore and macropore volume decreases. Due to the decrease of pore size, the SSA of 30-days reacted samples increases correspondingly and is lower (for the clay-rich sample) or higher (for the calcareous sample) than that of the unreacted samples. Findings improve our understanding of dynamic alteration of shale properties during production. 1. INTRODUCTION Energy demand will continuously grow owing to the increasing global population as well as energy consumption (EIA, 2023). On the other hand, shale gas and oil reshaped the energy market in the United States, enabling the United States to become a net-export of natural gas country in 2017 (EIA, 2023). However, shale reservoirs are challenging tight formations that are still poorly understood in the extraction and production of hydrocarbons (Ross and Bustin, 2009; Curtis et al., 2012; Xiong et al., 2015, 2021a; Li Y. et al., 2016; Gong et al., 2019a; Benge et al., 2021; Awejori et al., 2022; Huang et al., 2022). One of the most challenging topics is the rock-fluid interactions post hydraulic fracturing and its subsequent impacts on the pore structures of fractured formation matrix.

Xiong, Fengyang↗

DOE OSTI2024

Analysis and Mitigation of Cascading Outages Using an Interaction Graph Addressing Transient Stability

Cascading outages of power systems pose great threats to system security and reliability, potentially leading to large-scale blackouts. For analysis and mitigation of cascading outages, this paper proposes a transient stability-incorporated interaction graph. This graph statistically quantifies the interactions among line outages and instabilities of generators, which can model propagation paths and patterns of cascading outages. Compared with an interaction graph that only models line outages, this new interaction graph provides important insights on how transient instability occurs along with cascading outages. It also offers more effective strategies for mitigating outage propagation. The proposed interaction graph can be constructed from datasets of historical or simulated cascading events. It is demonstrated on an NPCC 140-bus system with mitigation strategies.

Guo, Zhenping↗

DOE OSTI2025

Selection Algorithm for electron neutrino charged current interactions in SBND

The Short Baseline Neutrino (SBN) program at Fermilab is a joint proposal by three experimental collaborations primarily for the investigation of the cause behind the low-energy electron-like event excess observed by the MiniBoone experiment. This dissertation focuses on the near detector of the project, named the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber apparatus which will conduct searches for sterile neutrinos in the mass range of 1 ${eV}^2/{c}^4$, as well as provide cross-section measurements for neutrino interactions in argon and perform other beyond the standard model studies.\\ \indent As is the case for all detectors in the program, the SBND will use the Booster Neutrino Beam as its source, which will provide it with both muon and electron neutrinos. Given that the ability to discern between the neutrino flavors will be crucial to the fulfillment of the detector's physics goals, the objective of this work is to provide the collaboration with a tool capable of doing so. As such, we here present the development process for an inclusive selection algorithm for the identification of electron neutrino charged current (CC) events regardless of their interaction channel. This is done through a combination of traditional techniques, such as the implementation of cuts on the reconstructed interaction properties, with the use of the Convolutional Visual Network, a machine learning algorithm capable of classifying particle interactions through the analysis of the topology of their final states. With this approach, we have developed a selection process that is capable of identifying $\nu_e$ CC interactions across a wide range of topologies with 34.4\% efficiency, as well as a purity of 91.2\%, making it especially promising for use in cross section studies.

Freire, Hector Moya [ABC Federal U.] (ORCID:000900↗

DOE OSTI2026

Measuring the neutrino-oxygen neutral current quasielastic cross section using the accelerator neutrino neutron interaction experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-doped water Cherenkov detector located on-axis to Fermilab’s Booster Neutrino Beam (BNB). ANNIE is uniquely positioned to perform high-statistics measurements of neutrino-nucleus interactions in water, benefiting from a large neutrino flux due to a short (100-meter) baseline. A central focus of ANNIE’s physics program is the measurement of both charged current (CC) and neutral current (NC) cross sections on water, including neutral current quasielastic (NCQE) and CC-inclusive channels. The NCQE measurement is particularly critical for constraining uncertainties in rare-event searches such as the Diffuse Supernova Neutrino Background (DSNB), where atmospheric $\nu$NCQE interactions constitute a significant and poorly constrained background. This dissertation presents a measurement of the flux-averaged neutrino-oxygen neutral current quasielastic ($\nu$NCQE) cross section using $2.573 \times 10^{20}$~POT of BNB exposure from the 2022 and 2023 beam years. The $\nu$NCQE interaction is identified through the primary $\gamma$-rays produced by nuclear de-excitation of the residual $^{15}$N$^*$ or $^{15}$O$^*$ nucleus following nucleon knockout from $^{16}$O. A dedicated Monte Carlo (MC) re-tuning campaign was conducted using an americium-beryllium (AmBe) calibration source, Michel electrons from stopped muons, and throughgoing dirt muons originating upstream of the detector. This multi-sample approach provided a wide-ranging $\mathcal{O}(\text{MeV})$--$\mathcal{O}(\text{GeV})$ dataset for tuning the simulated detector response, which was subsequently validated against AmBe neutron and Michel electron data for use in the $\nu$NCQE analysis. A dedicated laser calibration campaign was carried out to reduce timing uncertainties across the PMT system, enabling reconstruction of the BNB bunch substructure with sufficient resolution to serve as a background rejection tool. By selecting events in-time with individual neutrino bunches, beam-correlated $\nu$NCQE events are separated from diffuse and accelerator-induced backgrounds, notably skyshine neutrons and externally-originating events, that would otherwise dominate traditional charge-based selections within a small-scale, surface-level, short-baseline detector. A data-driven estimation of the skyshine neutron and external background rates was performed and incorporated into the systematic uncertainty budget. The flux-averaged $\nu$NCQE cross section on oxygen is measured to be $1.57 \pm 0.06\,(\text{stat.})$ $^{+0.91}_{-0.67}\,(\text{syst.})$ $\times 10^{-38}\ \text{cm}^{2}$. A full systematic budget is constructed by propagating uncertainties in the secondary hadronic interaction modeling, background cross section normalizations, detector response, neutrino flux, and the primary $\gamma$-ray emission probabilities from oxygen nuclear de-excitation. An idealized de-excitation model, constructed from existing measurements in the literature is developed to benchmark the predictions of the \textsc{GENIE} event generator. A comparison reveals that \textsc{GENIE} systematically overpredicts the primary $\gamma$-ray emission probability from oxygen de-excitation by a factor of $1.49\times$ for $E_\gamma > 6$~MeV and $3.07\times$ in the $3$--$6$~MeV band. This comparison motivates the dominant systematic uncertainty in this analysis, where a conservative uncertainty of $^{+39.9\%}_{-0\%}$ on the primary $\gamma$-ray signal prediction is assigned. The ANNIE result is consistent with and complementary to existing flux-averaged $\nu$NCQE cross section measurements from T2K and Super-Kamiokande, providing an independent measurement with a different detector, neutrino beam, and analysis methodology. Looking ahead, an upgrade to the ANNIE DAQ infrastructure enabling continuous extended readout will allow a complementary $\nu$NCQE neutron multiplicity measurement, directly relevant to constraining the NCQE background in DSNB searches, competitive with the recent T2K measurement at SK-Gd. The planned Super-SANDI upgrade, deploying a large Water-based Liquid Scintillator (WbLS) volume, will further extend ANNIE's reach to hadronic final states and exclusive NC channels, and enable joint measurements with liquid argon detectors sharing the BNB beamline ahead of DUNE and Hyper-Kamiokande.

Doran, Steven [Iowa State U.]↗

DOE OSTI2026

Pre-Equilibrium De-Excitations for Neutrino-Nucleus Interactions

The Deep Underground Neutrino Experiment (DUNE) is sensitive to MeV-scale energy depositions from low-energy astrophysical neutrinos, including those from core-collapse supernovae. Interpreting these detector signals requires accurate modeling of the nuclear de-excitation that follows from the neutrino-nucleus interaction. The MARLEY (Model of Argon Reaction Low-Energy Yields) event generator specializes in the low-energy regime. MARLEY currently assumes the residual nucleus equilibrates immediately after the primary interaction. This omits the intermediate pre-equilibrium stage in which energy redistributes among nucleons until statistical equilibrium is reached. While pre-equilibrium effects are well established for nucleon-induced reactions, they have not previously been studied for neutrino-nucleus interactions. This work addresses that gap by implementing a two-component exciton model, which is the first dedicated treatment of pre-equilibrium de-excitation for neutrino-nucleus interactions, restructured around MARLEY's existing class hierarchy to prepare for direct integration, including particle-hole state densities, internal transition rates, and pre-equilibrium particle emission. The calculations show encouraging agreement with the TALYS-2.2 nuclear reaction code for neutron-nucleus interactions. We further propose a concrete integration path into the full MARLEY event generator, including derived class structure and an extended event record for pre-equilibrium vertices in support of future reweighting. Remaining work focuses on refining the emission width calculation, adding $\gamma$-ray emission, and completing this integration to quantify the impact of pre-equilibrium effects on the expected low-energy neutrino signals in DUNE and similar experiments.

Visser, Erin [Michigan State U., East Lansing (mai↗

DOE OSTI2026

Investigating Interference Term in DUNE And SBND Neutrino Interactions with ACHILLES

Understanding neutrino-nucleus interactions is critical for conducting precise neutrino oscillation ex- periments. However, uncertainties in neutrino-nucleus cross section measurements and our incomplete understanding of nuclear effects remain a significant challenge in neutrino physics. In this paper, we investigate contributions from quantum interference between one-body and two-body interactions in charged-current quasi-elastic (CCQE) neutrino scattering amplitudes, using the ACHILLES event gen- erator. Simulations are performed using muon neutrino flux from Fermilab’s Booster Neutrino Beam (BNB), as well as Long-Baseline Neutrino Facility (LBNF) flux. We observe the neutrino flux interact- ing with Argon nuclei as detected by the Short Baseline Near Detector (SBND) and DUNE Near Detector (DUNE-ND), both Liquid Argon Time Projection Chambers (LArTPC). We focus on one muon and one proton final states with cascade interactions. We analyze multiple experimental observables including outgoing kinematic variables, energy-momentum transfer variables, and Transverse Kinematic Imbalance (TKI) variables. In this study, we also investigate the effects of PRISM (a method of sampling flux from multiple off-axis angles which creates different neutrino energy spectra). We find that the ratio of in- terference to quasi-elastic contributions gives a non-flat distribution across a 0-3.5 GeV energy range, indicating that effects of interference vary with the neutrino energy sampled. Our ultimate goal is to reduce systematic uncertainties from neutrino interactions when conducting oscillation experiments by working to disentangle the effects of quantum interference from the quasi-elastic signature.

Serumaga, Peera [Fermilab; UC, San Diego] (ORCID:0↗

DOE OSTI2025

Lassa virus protein–protein interactions as mediators of Lassa fever pathogenesis

Viral hemorrhagic Lassa fever (LF), caused by Lassa virus (LASV), is a significant public health concern endemic in West Africa with high morbidity and mortality rates, limited treatment options, and potential for international spread. Despite advances in interrogating its epidemiology and clinical manifestations, the molecular mechanisms driving pathogenesis of LASV and other arenaviruses remain incompletely understood. This review synthesizes current knowledge regarding the role of LASV host-virus interactions in mediating the pathogenesis of LF, with emphasis on interactions between viral and host proteins. Through investigation of these critical protein–protein interactions, we identify potential therapeutic targets and discuss their implications for development of medical countermeasures including antiviral drugs. This review provides an update in recent literature of significant LASV host-virus interactions important in informing the development of targeted therapies and improving clinical outcomes for LF patients. Knowledge gaps are highlighted as opportunities for future research efforts that would advance the field of LASV and arenavirus pathogenesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Fast Oxygen Redox Kinetics Induced by CoO 6 Octahedron With π –Interaction in P2–Type Sodium Oxides

Enhancing the kinetics of lattice oxygen redox (LOR) in P2-type layered sodium oxide cathodes is crucial for the advancement of sodium-ion batteries (SIBs) with superior energy and power densities. Electronic structure regulation stands out as a highly effective approach to address the inherent limitations of P2-type layered oxides with LOR, including sluggish kinetics, phase transitions, voltage hysteresis, and local structural distortion. In this work, a strategy involving the introduction of CoO 6 octahedra with π-interaction into Na 0.6 Li 0.1 Fe 0.3–x Co x Mn 0.6 O 2 (x = 0, 0.15, 0.3) cathodes to facilitate Na-ion transport is proposed. Furthermore, the impact of FeO 6 octahedra with σ-interaction in P2-type cathodes on electrochemical performance is comprehensively investigated. Through multimodal in-situ and ex-situ characterization techniques, it is revealed that Co–O with π-interaction effectively mitigates P2-OP4 phase transitions by strengthening Na–O, reduces voltage hysteresis, and stabilizes the local structure. Consequently, Na 0.6 Li 0.1 Co 0.3 Mn 0.6 O 2 demonstrates enhanced Na-ion diffusion kinetics, leading to improved rate performance and a reversible capacity of 55 mAh g –1 at 10 C, significantly outperforming cathodes with Fe–O σ-interaction. Moreover, when coupled with hard carbon, the full cell achieves a remarkable energy density of 395 Wh kg –1 (on cathode) at 0.1 C, with a capacity retention of 75% over 100 cycles at 1 C.

25 ENERGY STORAGE↗

DOE OSTI2025

Empirical evidence that glucan-interacting amino acid side chains within the transmembrane channel collectively facilitate cellulose synthase function

The fundamental mechanism of cellulose synthesis is widely conserved across Kingdoms and depends on cellulose synthases, which are processive, dual-function, family 2 glycosyltransferases (GT-2). These enzymes polymerize glucose on the cytoplasmic side of the plasma membrane and export the glucan chain to the cell surface through an integral transmembrane (TM) channel. Structural studies of active plant cellulose synthases (CESAs) have revealed interactions between the nascent glucan chain and the side chains of polar, charged, and aromatic amino acid residues that line the TM channel. However, the functional consequences of modifying these side chains have not been tested in vivo in CESAs or other processive GT-2s. To test this, we used an established in vivo assay based on genetic complementation of CESA5 in the moss, Physcomitrium patens. For accurate prediction of glucan-interacting amino acid residues, we generated a complete homotrimeric molecular model of PpCESA5 using a combination of homology and de novo modeling. All-atom molecular dynamics-based analyses of contact metrics and interaction energy identified 23 amino acid residues with high propensity to interact with the nascent glucan chain within the TM channel or on the apoplastic surface of PpCESA5. Mutating any one of 18 of these amino acid residues to alanine, thereby removing their side chains, abolished or impaired CESA function, with the strongest effects observed upon the loss of charged amino acid side chains. This provides direct evidence to support the hypothesis that multiple amino acid residues collectively maintain a smooth energy landscape within the TM channel to facilitate glucan translocation.

59 BASIC BIOLOGICAL SCIENCES↗