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DOE OSTI2020

Investigation of the effect of taurine supplementation on muscle taurine content in the mdx mouse model of Duchenne muscular dystrophy using chemically specific synchrotron imaging

Duchenne muscular dystrophy (DMD) is a lethal genetic muscle wasting disorder, which currently has no cure. Supplementation with the drug taurine has been shown to offer therapeutic benefit in the mdx model for DMD, however the mechanism by which taurine protects dystrophic muscle is not fully understood. Mdx muscle is deficient in taurine, however it is not known if this deficiency occurs in the extracellular space, in other cells present in the tissue (such as immune cells) or in the myofibre itself. Likewise, the tissue location of taurine enrichment in taurine treated mdx muscle is not known. In this study we applied X-ray absorption near edge spectroscopy (XANES) at the sulfur K-edge in an imaging format to determine taurine distribution in muscle tissue. XANES is the only technique currently capable of imaging taurine directly in muscle tissue, at a spatial resolution approaching myocyte cell size (20–50 μm). Using a multi-modal approach of XANES imaging and histology on the same tissue sections, we show that in mdx muscle, it is the myofibres that are deficient in taurine, and taurine supplementation ameliorates this deficiency. Increasing the taurine content of mdx myofibres was associated with a decrease in myofibre damage (as shown by the percentage of intact myofibres) and inflammation. These data presented here will help drive future studies to better elucidate the molecular mechanisms through which taurine protects dystrophic muscle; they also support the continued investigation of taurine as a therapeutic intervention for DMD.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Investigating Carboxysome Morphology Dynamics with a Rotationally Invariant Variational Autoencoder

Carboxysomes are a class of bacterial microcompartments that form proteinaceous organelles within the cytoplasm of cyanobacteria and play a central role in photosynthetic metabolism by defining a cellular microenvironment permissive to CO2 fixation. Critical aspects of the assembly of the carboxysomes remain relatively unknown, especially with regard to the dynamics of this microcompartment. Progress in understanding carboxysome dynamics is impeded in part because analysis of the subtle changes in carboxysome morphology with microscopy remains a low-throughput and subjective process. Here we use deep learning techniques, specifically a Rotationally Invariant Variational Autoencoder (rVAE), to analyze fluorescence microscopy images of cyanobacteria bearing a carboxysome reporter and quantitatively evaluate how carboxysome shell remodelling impacts subtle trends in the morphology of the microcompartment over time. Toward this goal, we use a recently developed tool to control endogenous protein levels, including carboxysomal components, in the model cyanobacterium Synechococcous elongatus PCC 7942. By utilization of this system, proteins that compose the carboxysome can be tuned in real time as a method to examine carboxysome dynamics. We find that rVAEs are able to assist in the quantitative evaluation of changes in carboxysome numbers, shape, and size over time. Further, we propose that rVAEs may be a useful tool to accelerate the analysis of carboxysome assembly and dynamics in response to genetic or environmental perturbation and may be more generally useful to probe regulatory processes involving a broader array of bacterial microcompartments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

An overview of switchgrass phenotypes variability across diverse populations and their implications for conversion to fuels

There have been substantial changes to the human lifestyle over the past two centuries, which are reflected in the amount of fuel we consume to power our day-to-day needs. The way we use these resources has indeed manifested in an overdependence on non-renewable energy sources, such as coal and petroleum, for generating electricity and powering our transportation needs. There is a pressing need to explore alternative ways of fueling our current lifestyle without impacting the environment. Biofuels have long been touted as a sustainable solution for use as drop-in fuels in aviation and maritime applications. Still, they have yet to establish themselves as a competitive commercial alternative, necessitating further research and development. Lignocellulosic biomass is an underutilized resource that is widely accessible for the commercial processing of renewable biofuels. Bioenergy crops, such as switchgrass (Panicum virgatum L.), which can be cultivated on marginal lands with minimal competition for agricultural land, are an ideal and promising candidate for bulk-scale biofuel synthesis. Over the past 30 years, significant progress has been made in breeding and genetically modifying these grasses to enhance their drought resilience and subsequent yields. However, discrepancies in biomass composition can lead to irregular feedstocks for downstream operations, which in turn affect overall production targets for biofuels. Here, this review examines the variability in switchgrass (P. virgatum L.) biomass phenotypes across diverse populations and plant components, and their implications for biofuel conversion. The study highlights significant variations in biomass yield, composition, and cell wall chemistry both between switchgrass genotypes and within individual cultivars. Key findings include differences in cellulose, hemicellulose, and lignin content between leaves and stems, which affect biomass digestibility and ethanol yield. The review also discusses the impact of lignin chemistry, particularly the syringyl/guaicyl (S/G) ratio, on the efficiency of biomass saccharification. Furthermore, it explores how these variations respond differently to various pretreatment techniques, affecting overall biofuel production. We conclude that understanding and quantifying this variability is crucial for optimizing switchgrass as a feedstock for commercial biofuel production, thereby potentially addressing the pressing need for sustainable energy sources in sectors such as aviation.

Kousika, Rohit [Univ. of Tennessee, Knoxville, TN ↗

DOE OSTI2021

High-throughput functional variant screens via in vivo production of single-stranded DNA

Significance We report a methodology for the pooled construction of mutants bearing precise genomic sequence variations and multiplex phenotypic characterization of these mutants using next-generation sequencing (NGS). Unlike existing techniques depending on CRISPR-Cas–directed genomic breaks for genome editing, this strategy instead uses single-stranded DNA produced by a retron element for recombineering. This enables libraries of millions of elements to be constructed and offers relaxed design constraints which permit natural DNA or random variation to be used as inputs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction

BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed. METHODS: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals. RESULTS: Here we identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction–associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression. CONCLUSIONS: Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Determination of a suitable molar absorption coefficient (ε) for lignin analysis of fibrous plants using the CASA method

In this study, the benchmarking and applicability of the CASA method for the analysis of fibrous plants, such as flax, hemp, and jute, is evaluated. Lignin is a phenolic biopolymer present in plant cell walls and is composed of three primary monomeric units, designated G, S, and H. Various factors, such as the genetic variability, influence the relative proportions of these units in plant samples. Recently, the cysteine-assisted sulfuric acid (CASA) method has been introduced as a rapid method for the quantification of lignin in wood samples. The aim of this study is to establish a suitable molar absorption coefficient (ε, L·g –1 ·cm –1 ) to adapt the CASA method for use with annual plant fibers. This investigation was motivated by the technical advantages of the CASA method, including higher throughput, lower reaction temperatures, and ecological benefits due to the absence of carcinogenic, mutagenic, or reprotoxic (CMR) substances and the need for minimal sample quantities. In this method, lignin solubilization is facilitated by cysteine, which is an amino acid that enhances the reaction kinetics, using a one-hour incubation period. However, as with any spectrophotometric technique, the CASA method depends on a molar absorption coefficient (ε) that varies according to the ratio of the aromatic units within the polymer. To evaluate the suitability of CASA for quantifying lignin in economically significant plant fibers, we investigate the impact of the unit ratios on the accuracy of ε. The results are compared with those of two widely recognized lignin analytical methods: the Klason method, which is a gravimetric reference method, and the acetyl bromide soluble lignin method, which is the most commonly used spectrophotometric approach. The final ε obtained in this study reveals a relative difference in lignin content ranging from –8 % to +9 % based on a comparison between the CASA and Klason methods across different industrial hemp varieties. As a result, using known G:S ratios in annual fibrous plants, ε can be estimated from our results.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Species-specific ribosomal RNA-FISH identifies interspecies cellular-material exchange, active-cell population dynamics and cellular localization of translation machinery in clostridial cultures and co-cultures

ABSTRACT The development of synthetic microbial consortia in recent years has revealed that complex interspecies interactions, notably the exchange of cytoplasmic material, exist even among organisms that originate from different ecological niches. Although morphogenetic characteristics, viable RNA and protein dyes, and fluorescent reporter proteins have played an essential role in exploring such interactions, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to further investigate interactions in synthetic or semisynthetic consortia. Despite its maturity, several challenges exist in using rRNA-FISH as a tool to quantify individual species population dynamics and interspecies interactions using high-throughput instrumentation such as flow cytometry. In this work, we resolve such challenges and apply rRNA-FISH to double and triple co-cultures of Clostridium acetobutylicum, Clostridium ljungdahlii, and Clostridium kluyveri . In pursuing our goal to capture each organism’s population dynamics, we demonstrate dynamic rRNA, and thus ribosome, exchange between the three species leading to the formation of hybrid cells. We also characterize the localization patterns of the translation machinery in the three species, identifying distinct, dynamic localization patterns among them. Our data also support the use of rRNA-FISH to assess the culture’s health and expansion potential, and, here again, our data find surprising differences among the three species examined. Taken together, our study argues for rRNA-FISH as a valuable and accessible tool for quantitative exploration of interspecies interactions, especially in organisms which cannot be genetically engineered or in consortia where selective pressures to maintain recombinant species cannot be used. IMPORTANCE Though dyes and fluorescent reporter proteins have played an essential role in identifying microbial species in co-cultures, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to quantitatively probe complex interactions between organisms in synthetic consortia. Despite its maturity, several challenges existed before rRNA-FISH could be used to study Clostridium co-cultures of interest. First, species-specific probes for Clostridium acetobutylicum and Clostridium ljungdahlii had not been developed. Second, “state-of-the-art” labeling protocols were tedious and often resulted in sample loss. Third, it was unclear if FISH was compatible with existing fluorescent reporter proteins. We resolved these key challenges and applied the technique to co-cultures of C. acetobutylicum , C. ljungdahlii , and Clostridium kluyveri . We demonstrate that rRNA-FISH is capable of identifying rRNA/ribosome exchange between the three organisms and characterized rRNA localization patterns in each. In combination with flow cytometry, rRNA-FISH can capture sub-population dynamics in co-cultures.

Hill, John D.↗

DOE OSTI2024

Advances in light system engineering across the phototrophic spectrum

Current work in photosynthetic engineering is progressing along the lines of cyanobacterial, microalgal, and plant research. These are interconnected through the fundamental mechanisms of photosynthesis and advances in one field can often be leveraged to improve another. It is worthwhile for researchers specializing in one or more of these systems to be aware of the work being done across the entire research space as parallel advances of techniques and experimental approaches can often be applied across the field of photosynthesis research. This review focuses on research published in recent years related to the light reactions of photosynthesis in cyanobacteria, eukaryotic algae, and plants. Highlighted are attempts to improve photosynthetic efficiency, and subsequent biomass production. Also discussed are studies on cross-field heterologous expression, and related work on augmented and novel light capture systems. This is reviewed in the context of translatability in research across diverse photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Light-Level Geolocation of the LANL Population of Western Bluebirds

Options for tracking and reconstructing animal movement are increasingly accessible due to rapidly decreasing costs, smaller sizes, and a proliferation of analytical inference techniques (Rutz and Hays, 2009; Wikelski et al., 2007). A wide variety of options exists for determining movement patterns of animals—from high-resolution pinpoint ARGOS satellite tags to time-consuming and logistically challenging radio telemetry. One option, light-level geolocation, offers small, affordable devices with long battery lives known as global location sensors (GLS), making them ideal for gathering preliminary migratory data. Geolocation works by inferring patterns of animal movement from light-level transitions between day and night (Hill and Braun, 2001). The recent advances in tracking technologies allow biologists to zoom in on migratory behavior, delineating heretofore-unidentified intraspecific migratory behavior (Delmore et al., 2012). The ubiquity of migratory divides (populations within a species that exhibit different migratory patterns) remains largely unknown, but their existence can lead to favorable demographic metrics (e.g., genetic diversity) in conservation contexts (Møller et al., 2011) and necessitate the development of population-specific conservation plans (Delmore et al., 2012). Delimiting the populations within a species that migrate and the extent of their migrations relative to individuals that remain resident across a species’ range has important evolutionary, ecological, and conservation implications. Within this context, Los Alamos National Laboratory (LANL) biologists have leveraged the local Avian Nestbox Network (ANN; Fair and Myers, 2002) to evaluate the migratory behaviors of a common local bird species, the Western Bluebird (Sialia mexicana).

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗

DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗

DOE OSTI2024

scMicrobe PTA: near complete genomes from single bacterial cells

Microbial genomes produced by standard single-cell amplification methods are largely incomplete. Here, we show that primary template-directed amplification (PTA), a novel single-cell amplification technique, generated nearly complete genomes from three bacterial isolate species. Furthermore, taxonomically diverse genomes recovered from aquatic and soil microbiomes using PTA had a median completeness of 81%, whereas genomes from standard multiple displacement amplification-based approaches were usually <30% complete. PTA-derived genomes also included more associated viruses and biosynthetic gene clusters.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Hydrogel-Immobilized Multienzyme Systems for Cell-Free Chemical Bioproduction

Cell-free gene expression systems derived from bacterial lysates enable the expression of biosynthetic pathways from inexpensive and easily prepared DNA templates. These systems hold great promise for modular and on-demand bioproduction of valuable small molecules in resource-limited settings but are constrained in their long-term stability, reusability, and deployability. In this work, we demonstrate that multiple cell-free expressed enzymes can be co-immobilized in biocompatible hydrogels made from poly(ethylene glycol) diacrylate (PEGDA) with added glycerol for enhanced gel integrity. Using small-angle X-ray scattering (SAXS), we show that the mesh size of PEGDA-glycerol hydrogels is comparable to the globular sizes of many proteins and enzymes, which could be used for protein entrapment. We found that the combination between entrapment and chemical ligation of the enzymes was effective to retain proteins. By employing a method for direct fluorescence measurement from hydrogels, we found that proteins can be retained in PEGDA-glycerol for at least a week. By separating the cell-free enzyme expression from the immobilization step, we successfully fabricated enzyme-laden hydrogels with three heterologous cell-free enzymes for the bioconversion of pyruvic acid to malic acid, an industrially valuable and versatile precursor chemical. Both heterologous and endogenous enzymes from the lysate remain functional in photo-cross-linked hydrogels and can be reused for multiple biocatalytic cycles. Moreover, we also found that the immobilized enzymes exhibit up to 1.6-fold higher activity and 2-fold longer lifetimes than free enzymes in liquid reactions. Furthermore, these results could advance the deployment of cell-free synthetic biology because they show that reusable, stable, and durable multienzyme systems can be created using readily available materials and fabrication techniques.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Terahertz time-domain spectroscopy imaging of pancreatic ductal adenocarcinoma tissues

Pancreatic ductal adenocarcinoma (PDAC) ranks among the malignancies with the highest fatality and morbidity rates. This is predominantly attributable to an absence of understanding the intricate and diverse microenvironment of the tumor. We use terahertz time-domain spectroscopy (THz-TDS) imaging in transmission geometry to probe ex-vivo the heterogenous microenvironment of the genetically modified murine PDAC tissue that closely resembles the PDAC heterogeneity in human malignancy. We introduced a maximum a-posteriori probability estimation algorithm to objectively the tumor’s heterogenous microenvironment using the average values of refractive index and absorption coefficient within the useable terahertz bandwidth as imaging markers. Furthermore, direct comparison of stained histopathologic images and the refractive index and the absorption coefficient high-resolution, two dimensional maps of the same PDAC samples confirms the high potential of the THz-TDS method for tumor tissue characterization.

absorption↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Enhanced resonant ultrasound spectroscopy for measurement of the elastic properties of multi-material systems

Understanding the elastic properties of materials is critical for their safe incorporation and predictable performance. Current methods of bulk elastic characterization often have notable limitations for in situ structural applications, with usage restricted to simple geometries and material distributions. To address these existing issues, this study sought to expand the capabilities of resonant ultrasound spectroscopy (RUS), an established nondestructive evaluation method, to include the characterization of isotropic multi-material samples. In this work, finite-element-based RUS analysis consisted of numerical simulations and experimental testing of composite samples comprised of material pairs with varying elasticity and density contrasts. Utilizing genetic algorithm inversion and mode matching, our results demonstrate that elastic properties of multi-material samples can be reliably identified within several percent of known or nominal values using a minimum number of identified resonance modes, given sample mass is held consistent. The accurate recovery of material properties for composite samples of varying material similarity and geometry expands the pool of viable samples for RUS and advances the method towards in situ inspection and evaluation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗