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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Near-Infrared Spectra of Type Ia Supernovae

We report near-infrared (NIR) spectroscopic observations of 12 'branch-normal' Type Ia supernovae (SNe Ia) that cover the wavelength region from 0.8 to 2.5 microns. Our sample more than doubles the number of SNe Ia with published NIR spectra within 3 weeks of maximum light. The epochs of observation range from 13 days before maximum light to 18 days after maximum light. A detailed model for a Type Ia supernovae is used to identify spectral features. The Doppler shifts of lines are measured to obtain the velocity and thus the radial distribution of elements. The NIR is an extremely useful tool to probe the chemical structure in the layers of SNe Ia ejecta. This wavelength region is optimal for examining certain products of the SNe Ia explosion that may be blended or obscured in other spectral regions. We identify spectral features from Mg II, Ca II, Si II, Fe II, Co II, Ni II, and possibly Mn II. We find no indications for hydrogen, helium, or carbon in the spectra. The spectral features reveal important clues about the physical characteristics of SNe Ia. We use the features to derive upper limits for the amount of unburned matter, to identify the transition regions from explosive carbon to oxygen burning and from partial to complete silicon burning, and to estimate the level of mixing during and after the explosion. Elements synthesized in the outer layers during the explosion appear to remain in distinct layers. That provides strong evidence for the presence of a detonation phase during the explosion as it occurs in delayed detonation or merger models. Mg II velocities are found to exceed 11,000 - 15,000 km/s, depending on the individual SNe Ia. That result suggests that burning during the explosion reaches the outermost layers of the progenitor and limits the amount of unburned material to less than 10% of the mass of the progenitor. Small residuals of unburned material are predicted by delayed detonation models but are inconsistent with pure deflagration or merger models. Differences in the spectra of the individual SNe Ia demonstrate the variety of these events.

Marion, G. H.↗
DOE OSTI2019

Chemosaturation Percutaneous Hepatic Perfusion (CS-PHP) with Melphalan: Evaluation of 2D-Perfusion Angiography (2D-PA) for Leakage Detection of the Venous Double-Balloon Catheter

Purpose: To evaluate the feasibility of 2D-perfusion angiography (2D-PA) for detecting leakage of the double-balloon catheter used for chemosaturation percutaneous hepatic perfusion (CS-PHP). Materials and Methods: Overall, 112 CS-PHP (09/2015-09/2018) in 52 patients were retrospectively screened for leakage alongside the double-balloon catheter on standard venograms. Finally, 18 procedures with visually detected leakage were included. Fifteen consecutive procedures without leakage served as control. To evaluate 2D-PA for leakage detection, the acquired digital subtraction venograms were post-processed. For each balloon, two different target ROIs were evaluated to assess a possible impact of localization and shape of the ROIs. Time to peak (TTP), peak density (PD), area under the curve (AUC), and ratios of target ROI/reference ROIs (PD{sub tROI}/PD{sub REF}; AUC{sub tROI}/AUC{sub REF}; and TTP{sub tROI}/TTP{sub REF}) were calculated. Results: Leakages were located as follows: 15/18 cranial and 3/18 caudal. At the cranial balloon both ROIs showed a significant decrease in PD{sub tROI}/PD{sub REF} and AUC{sub tROI}/AUC{sub REF} (ROI1: p < 0.0001; p < 0.0001; ROI2: p < 0.0001; p < 0.0001) and a significant increase in TTP{sub tROI}/TTP{sub REF} (ROI1: p = 0.0009; ROI2: p = 0.0003) after double-balloon correction. Following balloon adjustment, the 2D-PA ratios (PD and AUC) of the tested ROIs differed significantly (p < 0.05). The inter-individual comparison of the 2D-PA parameters of the group with leakage before balloon correction and the non-leakage group showed significantly different 2D-PA values for the cranial balloon in both ROIs (p < 0.05). No significant differences were found for the caudal balloon. Conclusion: 2D-PA provides a feasible tool for detecting leakages alongside the cranial portion of the double-balloon catheter used in CS-PHP. The shape and position of the ROIs used to assess perfusion and flow have an impact on the measurements.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1999

Sleep-wake differences in scaling behavior of the human heartbeat: analysis of terrestrial and long-term space flight data

We compare scaling properties of the cardiac dynamics during sleep and wake periods for healthy individuals, cosmonauts during orbital flight, and subjects with severe heart disease. For all three groups, we find a greater degree of anticorrelation in the heartbeat fluctuations during sleep compared to wake periods. The sleep-wake difference in the scaling exponents for the three groups is comparable to the difference between healthy and diseased individuals. The observed scaling differences are not accounted for simply by different levels of activity, but appear related to intrinsic changes in the neuroautonomic control of the heartbeat.

Flight Experiment↗
DOE OSTI2016

MOLECULAR GAS VELOCITY DISPERSIONS IN THE ANDROMEDA GALAXY

In order to characterize the distribution of molecular gas in spiral galaxies, we study the line profiles of CO (1 – 0) emission in Andromeda, our nearest massive spiral galaxy. We compare observations performed with the IRAM 30 m single-dish telescope and with the CARMA interferometer at a common resolution of 23 arcsec ≈ 85 pc × 350 pc and 2.5 km s{sup −1}. When fitting a single Gaussian component to individual spectra, the line profile of the single dish data is a factor of 1.5 ± 0.4 larger than the interferometric data one. This ratio in line widths is surprisingly similar to the ratios previously observed in two other nearby spirals, NGC 4736 and NGC 5055, but measured at ∼0.5–1 kpc spatial scale. In order to study the origin of the different line widths, we stack the individual spectra in five bins of increasing peak intensity and fit two Gaussian components to the stacked spectra. We find a unique narrow component of FWHM = 7.5 ± 0.4 km s{sup −1} visible in both the single dish and the interferometric data. In addition, a broad component with FWHM = 14.4 ± 1.5 km s{sup −1} is present in the single-dish data, but cannot be identified in the interferometric data. We interpret this additional broad line width component detected by the single dish as a low brightness molecular gas component that is extended on spatial scales >0.5 kpc, and thus filtered out by the interferometer. We search for evidence of line broadening by stellar feedback across a range of star formation rates but find no such evidence on ∼100 pc spatial scale when characterizing the line profile by a single Gaussian component.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

Coupled Stochastic Time-Inverted Lagrangian Transport/Weather Forecast and Research/Vegetation Photosynthesis and Respiration Model: Simulations of Tower-Based and Airborne CO2 Measurements - Part II

This paper evaluates simulations of atmospheric CO2 measured in 2004 at continental surface and airborne receptors, intended to test the capability to use data with high temporal and spatial resolution for analyses of carbon sources and sinks at regional and continental scales. The simulations were performed using the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Forecast and Research (WRF) model, and linked to surface fluxes from the satellite-driven Vegetation Photosynthesis and Respiration Model (VPRM). The simulations provide detailed representations of hourly CO2 tower data and reproduce the shapes of airborne vertical profiles with high fidelity. WRF meteorology gives superior model performance compared with standard meteorological products, and the impact of including WRF convective mass fluxes in the STILT trajectory calculations is significant in individual cases. Important biases in the simulation are associated with the nighttime CO2 build-up and subsequent morning transition to convective conditions, and with errors in the advected lateral boundary condition. Comparison of STILT simulations driven by the WRF model against those driven by the Brazilian variant of the Regional Atmospheric Modeling System (BRAMS) shows that model-to-model differences are smaller than between an individual transport model and observations, pointing to systematic errors in the simulated transport. Future developments in the WRF model s data assimilation capabilities, basic research into the fundamental aspects of trajectory calculations, and intercomparison studies involving other transport models, are possible venues for reducing these errors. Overall, the STILT/WRF/VPRM offers a powerful tool for continental and regional scale carbon flux estimates.

Eluszkiewicz, Janusz↗
DOE OSTI2021

The use and abuse of cinnabar in Late Neolithic and Copper Age Iberia

Abstract In this study, total mercury (THg) was analyzed in archaeological human bone from 23 sites dating to between the Middle Neolithic and the Antiquity. A total of 370 individuals from individual or collective burials was sampled, mostly using cortical bone from the humerus. These individuals were recovered from over 50 different funerary structures ranging from tholoi, pits, caves, and hypogea. Although cinnabar (HgS) is a likely cause of mercury poisoning and toxicity for people exposed to this mineral from mining or use as a paint or pigment, not all sites investigated here had cinnabar associated with the burials or other excavated areas. We found unusual levels of THg in many of the sampled individuals that we assume were caused by exposure to cinnabar in life, and not by diagenetic processes or other exposures to mercury such as through diet, which would only cause negligible accumulation of THg in bone. Our data, based on the largest sampling ever undertaken on contamination of human bone through archaeological evidence, provide a baseline for additional research on cinnabar and its use in Prehistory. Moderate to high levels of THg in human bone are mainly associated with societies dating from the second half of the 4th to late 3rd millennia B.C. (Late Neolithic to Middle Chalcolithic) in southern Iberia. By the Late Chalcolithic and Early Bronze Age, the use of cinnabar decreased significantly and became minimal or absent. The use and abuse of cinnabar appears to have been pervasive throughout the above‐mentioned period and particularly between c. 2900–2300 B.C. This occurred in connection with the high symbolic and probably sacred value of the substance, which was sought after, traded, and extensively used in a variety of rituals and social practices.

Emslie, Steven D.↗
DOE OSTI2023

Physical Properties of Moist, Fermented Corn Kernels

A novel approach to producing corn stover biomass feedstock has been investigated. In this approach, corn grain and stover are co-harvested at moisture contents much less than typical corn silage. The grain and stover are conserved together by anaerobic storage and fermentation and then separated before end use. When separated from the stover, the moist, fermented grain had physical characteristics that differ from typical low-moisture, unfermented grain. A comprehensive study was conducted to quantify the physical properties of this moist, fermented grain. Six corn kernel treatments, either fermented or unfermented, having different moisture contents, were used. Moist, fermented kernels (26 and 36% w.b. moisture content) increased in size during storage. The fermented kernels’ widths and thicknesses were 10% and 15% greater, respectively, and their volume was 28% greater than the dry kernels (15% w.b.). Dry basis particle density was 9% less for moist, fermented kernels. Additionally, the dry basis bulk density was 29% less, and the dry basis hopper-discharged mass flow rate was 36% less. Moist, fermented grain had significantly greater kernel-to-kernel coefficients of friction and angles of repose compared to relatively dry grain. The friction coefficient on four different surfaces was also significantly greater for fermented kernels. Fermented corn kernels had lower individual kernel rupture strengths than unfermented kernels. These physical differences must be considered when designing material handling and processing systems for moist, fermented corn grain.

09 BIOMASS FUELS↗
DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗
DOE OSTI2025

Swing Contract-Based Valuation for Distributed Energy Resources in Transactive Energy Systems: A Reinforcement Learning Approach

With the proliferation of distributed energy resources (DERs) and power grids with high fractions of renewable energy, market constructs are evolving to allow DERs to participate in multiple possible markets, at different levels of grid hierarchy. The effective participation of DERs in market environments is aided by swing contract-based pricing mechanisms, whereby DERs have a two-part compensation structure – one for their reservation/commitment and another for performancedriven ex-post payment for their actual mobilization during dispatch. In this paper, we propose a reinforcement learningbased (Q-learning) approach that allows a rational DER agent to select the market it wants to participate in within a composite market environment where individual markets are coordinated by possibly different actors. The proposed Q-learning framework aids DERs in their self-valuation by implicitly maximizing their own payoff through market participation, assuming a swing contract-based compensation structure. We complement our work through simulation-based investigations where factors affecting the DER decision making process, such as parametric uncertainties in market (and grid) environments, are studied.

Naqvi, Syed Ahsan Raza↗
DOE OSTI2022

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Control of light-matter interactions in hybrid structured environments with novel nanomaterials of different dimensionalities

The project calls for exploration of individual nanomaterials and the ability to fabricate functional structures without degrading the properties of components. The rich photophysics of novel semiconductors like transition metal dichalcogenides (TMDs), lead halide perovskites (LHPs) and nanocrystal quantum dots (NQDs) can be gainfully exploited in integrated photonic structures alongside conventional semiconducting and dielectric materials. Realization of the prospects of such hybrid structures crucially depends on the ability to fabricate these “designer” assemblies while preserving and enhancing the optoelectronic properties of individual components. We study emission properties of these materials at both bulk and single particles levels to elucidate their intrinsic photophysical properties in order to minimize energy losses and facilitate their judicious integration via control of energy/charge flows and photon-exciton interconversions.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Variability in Galactic Cosmic Radiation- Induced DNA Damage Response in Inbred Mice Is Modulated by Genetics

In radiation biology, the ability to predict cancer risk associated with exposure to low doses of high-LET (Linear Energy Transfer) ionizing radiation remains a challenge. Epidemiological methods lack the sensitivity and power to provide detailed risk estimates for cancer and ignore individual sensitivity. We have hypothesized that DNA repair capacity is the primary factor differentiating peoples radiation sensitivity. We previously showed in immortalized human cell lines that characterizing the dose and time dependence of p53-binding protein 1 (53BP1) foci formation in the nucleus following X-rays exposure is sufficient to predict DNA repair response to any other LET in the same cell line. We now tested this hypothesis across a population of mice with different genetic background. Fibroblast cells were extracted and cultivated from 76 individual mice from 15 different strains and exposed to HZE (high (H) atomic number (Z) and energy (E) galactic cosmic ray particles) particles and X-rays. Individual radiation sensitivities were investigated by high throughput measurement of DNA repair kinetics that evaluated 53bp1 foci numbers as a surrogate for DNA double-strand breaks at various times post-irradiation. Instead of just counting foci which can be hard to distinguish for high-LET or high doses, we also took into account the track structure of high-LET particles to compute the remaining number of unrepaired tracks as a function of time post-irradiation. As expected, the percentage of unrepaired track over a 48 hours follow-up period increased with LET. In addition, repair rate was modulated by genetics, with animals from the same strain showing small variance while large rate differences were observed between strains. Radiation strain sensitivity ranking was estimated based on repair rates from exposure to each LET evaluated in this work, and ranking for high-LET correlated better with ranking from high dose of X-ray, not low dose. At the in-vivo level, drops in T-cells and B-cells number measured 24 hours after 0.1 Gy (Gray) X-ray exposure, correlated with slower DNA repair kinetic in fibroblast cells of the same strains of mice. At the genomic level, mouse genome wide association (GWA) analysis identified seven significant genetic loci on chromosomes 2, 3, 7, 10, 11, 13 and 19 with different significance depending on the LET. Interestingly, for the two highest LET, a common locus on Chromosome 10 was identified with high enrichment for DNA repair associated genes.Overall, this work suggests that repair kinetics of primary skin fibroblasts is a good surrogate marker for in-vivo radiation sensitivities in other tissues and that this response is modulated by genetics. Our study also confirms that DNA repair kinetics following high doses of X-ray can be used to predict radiation sensitivity to high-LET.

High-LET Radiation↗
DOE OSTI2017

Temporal effects of disturbance on community composition in simulated stage‐structured plant communities

Abstract In an era of global environmental change, understanding how disturbance affects the dynamics of ecological communities is crucial. However, few studies have theoretically explored the potential influence of disturbance including both intensity and frequency on compositional change over time in communities with stage structure. A spatially explicit, individual‐based model was constructed incorporating the various demographic responses to disturbance of plants at two different growth stages: seedlings and adults. In the model, we assumed that individuals within each stage were demographically equivalent (neutral) but differed between stages. We simulated a common phenomenon that seedlings suffered more from disturbance such as grazing and fire than adults. We showed how stage‐structured communities of seedlings and adults responded to disturbance with various levels of disturbance frequency and intensity. In “undisturbed” simulations, the relationship between average species abundance (defined here as the total number of individuals divided by species richness) and community composition turnover (measured by the Bray–Curtis similarity index) was asymptotic. However, in strongly “disturbed” simulations with the between‐disturbance intervals greater than one, this relationship became unimodal. Stage‐dependent response to disturbance underlay the above discrepancy between undisturbed and disturbed communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mechanical Modeling and Testing of Pouch Cells under Various Loading Conditions

Understanding the mechanical behaviors of batteries is critical to improve battery safety since mechanical failure may directly lead to short-circuits. Nevertheless, mechanical modeling of batteries is challenging since a cell usually contains multiple thin layers with drastically different material properties, and they exhibit different responses under different loading conditions. In this work, we developed a mechanical model for a large-format pouch cell, where the cell is represented by thick shell elements that are not only computationally efficient but also account for different thickness and material properties of individual components. Moreover, the mechanical properties of active materials in electrodes are described by a continuous surface cap model that can capture both compaction and shear deformation modes and can include strain rate effect and damage. Furthermore, to evaluate the model capabilities, we conducted abuse tests under various loading conditions (quasi-static compression, shear and impact). It is shown that the model prediction of load-displacement relationship and failure condition agree with experimental results, which demonstrates that the developed model can capture the mechanical behaviors of a cell in different abuse events. Details of element formulation, material parameters evaluation and test setup are presented. Capabilities, limitations and future directions of model development are also discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Although accurately classifying signals from earthquakes and explosions at local distance (<250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.

58 GEOSCIENCES↗
DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2009

Measurements of Lunar Dust Charging Properties by Electron Impact

Dust grains in the lunar environment are believed to be electrostatically charged predominantly by photoelectric emissions resulting from solar UV radiation on the dayside, and on the nightside by interaction with electrons in the solar wind plasma. In the high vacuum environment on the lunar surface with virtually no atmosphere, the positive and negative charge states of micron/submicron dust grains lead to some unusual physical and dynamical dust phenomena. Knowledge of the electrostatic charging properties of dust grains in the lunar environment is required for addressing their hazardous effect on the humans and mechanical systems. It is well recognized that the charging properties of individual small micron size dust grains are substantially different from the measurements on bulk materials. In this paper we present the results of measurements on charging of individual Apollo 11 and Apollo 17 dust grains by exposing them to mono-energetic electron beams in the 10-100 eV energy range. The charging/discharging rates of positively and negatively charged particles of approx. 0.1 to 5 micron radii are discussed in terms of the sticking efficiencies and secondary electron yields. The secondary electron emission process is found to be a complex and effective charging/discharging mechanism for incident electron energies as low as 10-25 eV, with a strong dependence on particle size. Implications of the laboratory measurements on the nature of dust grain charging in the lunar environment are discussed.

Abbas, Mian M.↗
DOE OSTI2024

Age over sex: evaluating gut microbiota differences in healthy Chinese populations

Age and gender have been recognized as two pivotal covariates affecting the composition of the gut microbiota. However, their mediated variations in microbiota seem to be inconsistent across different countries and races. In this study, 613 individuals, whom we referred to as the “healthy” population, were selected from 1,018 volunteers through rigorous selection using 16S rRNA sequencing. Three enterotypes were identified, namely, Escherichia–Shigella , mixture ( Bacteroides and Faecalibacterium ), and Prevotella . Moreover, 11 covariates that explain the differences in microbiota were determined, with age being the predominant factor. Furthermore, age-related differences in alpha diversity, beta diversity, and core genera were observed in our cohort. Remarkably, after adjusting for 10 covariates other than age, abundant genera that differed between age groups were demonstrated. In contrast, minimal differences in alpha diversity, beta diversity, and differentially abundant genera were observed between male and female individuals. Furthermore, we also demonstrated the age trajectories of several well-known beneficial genera, lipopolysaccharide (LPS)-producing genera, and short-chain fatty acids (SCFAs)-producing genera. Overall, our study further elucidated the effects mediated by age and gender on microbiota differences, which are of significant importance for a comprehensive understanding of the gut microbiome spectrum in healthy individuals.

Wu, Jiacheng↗