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DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗

DOE OSTI2023

Compositionally Complex Alloys: Some Insights from Photoemission Spectroscopy

Photoemission spectroscopy (PES) is an underrepresented part of current and past studies of compositionally complex alloys (CCA) such as high-entropy alloys (HEA) and their derivatives. PES studies are very important for understanding the electronic structure of materials, and are therefore essential in some cases for a correct description of the intrinsic properties of CCAs. Here, we present several examples showing the importance of PES. First, we show how the difference between the split-band structure and the common-band structure of the valence band (VB), observed by PES, can explain a range of properties of CCAs and alloys in general. A simple description of the band crossing in CCAs composed from the early and late transition metals showing a split band is discussed. We also demonstrate how a high-accuracy PES study can determine the variation in the density of states at the Fermi level as a function of Cu content in Ti-Zr-Nb-Ni-Cu metallic glasses. Finally, the first results of an attempt to single out the contributions of particular constituents in Cantor-type alloys to their VBs are presented. The basic principles of PES, the techniques employed in studies presented, and some issues associated with PES measurements are also described.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A double copy from twisted (co)homology at genus g

We study a family of generalized hypergeometric integrals defined on punctured Riemann surfaces of genus g. These integrals are closely related to g-loop string amplitudes in chiral splitting, where one leaves the loop-momenta, moduli and all but one puncture un-integrated. We study the twisted homology groups associated to these integrals, and determine their intersection numbers. We make use of these homology intersection numbers to write a double-copy formula for the "complex" version of these integrals -- their closed-string analogues. To verify our findings, we develop numerical tools for the evaluation of the integrals in this work. This includes the recently introduced Enriquez kernels -- integration kernels for higher-genus polylogarithms.

Pokraka, Andrejz [Brown University; Amsterdam Univ↗

DOE OSTI2022

Machine learning-assisted elucidation of CD81–CD44 interactions in promoting cancer stemness and extracellular vesicle integrity

Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

SWA_Challenges_MF_ESMFNC_ZERH

This study completed by Steven Winter Associates documents the challenge that exist for existing multi family buildings to achieve Energy Star for new homes or Zero Energy Ready Home Certification.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

This project demonstrates a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique is used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that laser ultrasonics-based methodology developed in this work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

The primary objective of this seed project is to develop a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore, serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique will be used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that the products of the proposed work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Thermodynamic Model and Initial Experimental Investigation of Air Dehumidification through Electrically Charged Vapor Capturing Electrostatic Droplets

The removal of water vapor from the air to reduce relative humidity is a well known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation with the goal to reduce the energy consumption associated with dehumidification. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. In this thesis, a broad theoretical investigation to study the electrostatically enhanced condensation processes was carried out. These processes consist of the use of highly charged particles, preferably highly charged water droplets to attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. The electrical charge promotes the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation on curved surfaces and, consequently, the equilibrium between evaporation and condensation is shifted towards condensation. This investigation resulted in the development of a thermodynamics model, which was able to predict an effective size range of the charged droplets for optimal dehumidification, under ideal conditions. The range resulted in about 2 to 4 m in diameter, while the electrical charge was kept to the maximum limit predicted by the Rayleigh model. A sensitivity analysis on the variation of the size and the charge of the charged droplets was also performed. In terms of dehumidification rates, when six electrospray heads, i.e. the maximum number of heads tested in the experimental campaign, were considered, the model predicted very limited rates, way lower than the target of this research, i.e. a dehumidification rate in terms of relative humidity of 5 % with an air flow rate of 5 cfm. While ways to increase this rate existed, their implementation proved difficult for this initial investigation. According to the requirements predicted by the model, the use of electrosprays appeared the most suitable solution for the production of small but highly charged droplets. The electrospray features and operational modes were studied in detail and an electrospray assembly was designed to be tested in this initial experimental investigation. For this initial investigation, an experimental system was designed and built to validate the thermodynamics model and with the goal to achieve a dehumidification rate of 5 % in terms of relative humidity with an air flow rate of 5 cfm. The experimental system consisted of a wind tunnel test apparatus, dew point sensors, thermocouples for temperature measurements, pressure sensors and other devices. The experimental system was equipped with a computerized data acquisition and storage system. The system was suitable to evaluate the air water content differential before and after the test section where the electrospray heads were installed. The air water content differential was evaluated in terms of the ∆ω/ω_1 ratio, to eliminate the dependence on the dry bulb temperature along the test apparatus. This work presented initial experimental data for electrostatically enhanced dehumidification processes. Working parameters, such as air and water flow rates, high voltage potentials and polarity, deionized water types with different electrical conductivity, were varied to find the best combination for an improved dehumidification. With the set up used and the conditions considered, it was never possible to achieve a 5 % dehumidification rate with 5 cfm. In general, the dehumidification rate was always limited and lower than 1 % for all the air flow rates considered and mainly within the uncertainty of the dew point sensors. These results confirmed the already limited predictions of the thermodynamics model, for ideal conditions and the interesting and promising results obtained in some experimental investigations available in the open literature were not achievable in this thesis. The more probable reasons were the higher air flow rates considered and the complete absence of the use of cooling power, which was mainly implemented to facilitate the vapor condensation.

Morcelli, Stefano↗

DOE OSTI2023

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Abstract Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Association of human T-cell leukemia virus type 1 with prevalent rheumatoid arthritis among atomic bomb survivors: A cross-sectional study

Previous studies have suggested that human T-cell leukemia virus type 1 (HTLV-1) might act as a pathogen in rheumatoid arthritis (RA), but epidemiological evidence of an association is scarce. We measured anti-HTLV-1 antibodies among Nagasaki atomic bomb survivors to determine whether HTLV-1 is related to RA and whether radiation exposure is associated with HTLV-1 and RA prevalence. This is a cross-sectional study among atomic bomb survivors who participated in biennial health examinations from 2006 to 2010. Serum levels of anti-HTLV-1 antibodies were measured using a chemiluminescent enzyme immunoassay and confirmed by Western blotting. Association between HTLV-1 and RA was analyzed by a logistic regression model. Of 2091 participants (women 61.5%; median age, 73 years), 215 (10.3%) had anti-HTLV-1 antibodies. HTLV-1 prevalence was higher among women (13.1% vs 5.8%; P<.001). Twenty-two participants (1.1%) were diagnosed with RA. HTLV-1 prevalence among RA participants was significantly higher than that among non-RA participants (27.3% vs 10.1%; P=.020). After adjustment for age, sex, and hepatitis C virus infection, HTLV-1 was significantly associated with prevalent RA (odds ratio, 2.89; 95% confidence interval, 1.06, 7.03). There was no association between radiation dose and either the prevalence of HTLV-1 or RA. This study, among a well-defined group of atomic bomb survivors, suggests that HTLV-1 is associated with RA. Abbreviations: AHS = Adult Health Study, ATL/ATLL = adult T cell leukemia/lymphoma, bioDMARDs = biological disease modifying antirheumatic drugs, CCP = cyclic citrullinated peptide, CI = confidence interval, CLEIA = chemiluminescent enzyme immunoassay, CRP = C-reactive protein, csDMARDs = conventional synthetic disease modifying antirheumatic drugs, Gy = gray, HCV = hepatitis C virus, HTLV-1 = human T-cell leukemia virus type 1, IF = immunofluorescent, RA = rheumatoid arthritis, RERF = Radiation Effects Research Foundation, RF = rheumatoid factor, SS = Sjogren syndrome.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Soil Particulate Organic Matter is Related to Ericoid Mycorrhizal Shrubs, not Ectomycorrhizal Trees, in a Temperate Forest

Mycorrhizal associations are key drivers of soil biogeochemistry, but previous studies have focused almost exclusively on ectomycorrhizal (EcM) and arbuscular mycorrhizal (AM) associations. Ericoid mycorrhizal (ErM) shrubs frequently occur in forest understories and are expanding in response to disturbance, but are rarely considered in biogeochemical frameworks. We investigated the relationships of understory ErM shrubs and overstory trees on carbon (C) and nitrogen (N) in soil organic matter fractions in a southern Appalachian temperate forest. We sampled the 0–10 cm mineral soil layer from 43 plots at the Coweeta Hydrologic Laboratory, across gradients in overstory EcM dominance and understory ErM shrub biomass. Soil C:N ratios increased with both increasing EcM dominance and increasing ErM shrub biomass. However, total particulate organic matter (POM) C, and the proportion of C and N held in POM increased with increasing ErM shrub biomass, but not with increasing EcM dominance. In contrast, mineral-associated organic matter (MAOM) C and N were negatively associated with EcM dominance, but were not related to ErM shrubs. Our findings suggest that ErM shrubs facilitate POM formation while AM trees promote MAOM formation. Because ErM shrub biomass represents a small fraction of total forest biomass, our work provides evidence that ErM shrubs have an outsized effect on soil organic matter, which advocates for their inclusion in mechanistic studies and biogeochemical frameworks.

Bonilla, Kayla A. [University of Georgia, Athens, ↗

DOE OSTI2025

Overcoming Data Scarcity in Carbon Storage Assessment: Estimating Petrophysical Properties in Legacy Wells Integrating Sample Logs and Geostatistical Approaches

Geological CO2 sequestration feasibility studies in mature basins frequently face challenges associated with legacy well data, which often lack the comprehensive logging suites required for accurate reservoir characterization. This study evaluates the storage potential of Ordovician– Devonian formations in a filed in the Delaware Basin portion of the Permian Basin, using a dataset of 47 wells.

58 GEOSCIENCES↗

DOE OSTI2021

Structure of Diisobutylene Maleic Acid Copolymer (DIBMA) and Its Lipid Particle as a “Stealth” Membrane-Mimetic for Membrane Protein Research

The study of membrane proteins remains challenging, especially in a native membrane environment. Recently, major progress has been made using maleic acid copolymers, such as styrene maleic acid, to purify membrane proteins and study them directly with native lipids associated with the membrane. Additional maleic acid copolymers, such as diisobutylene maleic acid (DIBMA) membrane-mimetic systems, are being developed and found to have improved spectroscopic properties and pH stability. We studied DIBMA and its lipid particles in solution to better understand its assembly, without and with the lipids, to provide an insight regarding how to use it in solution for better membrane extraction. Using small-angle neutron and X-ray scattering (SANS/SAXS), we show that DIBMA organizes into structures of different size scales at various concentrations and ionic strengths. The polymer performed reasonably well under most solvent conditions except in very low concentrations and high-salt conditions that could result in limited interaction with lipids. To explore DIBMA lipid particles as a suitable membrane-mimetic system for neutron scattering studies of membrane proteins, we measured and determined the contrast-matching point of DIBMA to be ~12% (v/v) D 2 O — similar to that of most protiated lipid molecules but distinct from that of regular protiated proteins — providing a natural contrast for separating their neutron scattering signals. Using SANS contrast variation, we demonstrated that the scattering from the whole lipid particle can be annihilated. Further, we determined that a well-defined lipid nanodisc structure with DIBMA was contrast-matched. These results demonstrate that the DIBMA lipid particle is an outstanding “stealth” membrane-mimetic for membrane proteins. The results provide a structural framework for understanding the organization and assembly process of the polymer itself and the lipid molecules. Such an understanding is imperative for structural techniques such as cryo-electron microscopy, nuclear magnetic resonance, small-angle scattering, and other biophysical techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Pulse profile modelling of thermonuclear burst oscillations – II. Handling variability

ABSTRACT Pulse profile modelling is a relativistic ray-tracing technique that can be used to infer masses, radii, and geometric parameters of neutron stars. In a previous study, we looked at the performance of this technique when applied to thermonuclear burst oscillations from accreting neutron stars. That study showed that ignoring the variability associated with burst oscillation sources resulted in significant biases in the inferred mass and radius, particularly for the high count rates that are nominally required to obtain meaningful constraints. In this follow-on study, we show that the bias can be mitigated by slicing the bursts into shorter segments where variability can be neglected, and jointly fitting the segments. Using this approach, the systematic uncertainties on the mass and radius are brought within the range of the statistical uncertainty. With about 106 source counts, this yields uncertainties of approximately 10 per cent for both the mass and radius. However, this modelling strategy requires substantial computational resources. We also confirm that the posterior distributions of the mass and radius obtained from multiple bursts of the same source can be merged to produce outcomes comparable to that of a single burst with an equivalent total number of counts.

Astronomy & Astrophysics↗

DOE OSTI2025

The relationship between below average cognitive ability at age 5 years and the child’s experience of school at age 9

Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.

Bowe, Andrea K.↗

DOE OSTI2024

Estradiol associations with brain functional connectivity in postmenopausal women

Abstract Objective Previous studies have found that estrogens play a role in functional connectivity in the brain; however, little research has been done regarding how estradiol is associated with functional connectivity in postmenopausal women. The purpose of this study was to examine the relationship between estradiol and functional connectivity in postmenopausal women. Methods Structural and blood oxygenation level–dependent resting-state magnetic resonance imaging scans of 88 cognitively healthy postmenopausal individuals were obtained along with blood samples collected the same day as the magnetic resonance imaging to assess hormone levels. We generated connectivity values in CONN toolbox version 20.b, an SPM-based software. Results A regression analysis was run using estradiol level and regions of interest (ROI), including the hippocampus, parahippocampus, dorsolateral prefrontal cortex, and precuneus. Estradiol level was found to enhance parahippocampal gyrus anterior division left functional connectivity during ROI-to-ROI regression analysis. Estradiol enhanced functional connectivity between the parahippocampal gyrus anterior division left and the precuneus as well as the parahippocampal gyrus anterior division left and parahippocampal gyrus posterior division right. An exploratory analysis showed that years since the final menstrual period was related to enhanced connectivity between regions within the frontoparietal network. Conclusions These results illustrated the relationship between estradiol level and functional connectivity in postmenopausal women. They have implications for understanding how the functioning of the brain changes for individuals after menopause that may eventually lead to changes in cognition and behavior in older ages.

Obstetrics & Gynecology↗

DOE OSTI2021

1D kinetic study of pinch formation in a dense plasma focus: Transition from collisional to collisionless regimes

Here, the pinch-formation stage of a deuterium dense plasma focus, and associated “shock-flash” neutron yield, is studied using 1D kinetic simulations considering a plasma column with initial pressure P, initial radius R, and the compression to be driven by a constant current I. The relative behavior of the compression is shown to be similar for fixed ratios of the characteristic ion mean free path to the radius of the plasma column at stagnation, $λ_{st}/R_{st}$. This dimensionless parameter is shown to scale like $I^4/(P^3R^5)$. The compression ratio, $R/R_{st}$, is found to be a minimum when $λ_{st}/R_{st} ≈$ 1 and is the largest in the collisionless limit where $λ_{st} \gg R_{st}$. This behavior is in contrast to the analogous planar pinch where R/Rst decreases from one constant for λst/Rst$\ll$1 to a smaller constant for $λ_{st}/R_{st} \gg$ 1. The yield in the collisionless regime is shown to fall between the two well-known $I^4$ scaling laws. Furthermore, this regime exhibits qualities that potentially make it appealing for radiography applications, such as increased localization in time and space of the neutron formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗