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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Spatially explicit network analysis reveals multi‐species annual cycle movement patterns of sea ducks

Abstract Conservation of long‐distance migratory species poses unique challenges. Migratory connectivity, that is, the extent to which groupings of individuals at breeding sites are maintained in wintering areas, is frequently used to evaluate population structure and assess use of key habitat areas. However, for species with complex or variable annual cycle movements, this traditional bimodal framework of migratory connectivity may be overly simplistic. Like many other waterfowl, sea ducks often travel to specific pre‐ and post‐breeding sites outside their nesting and wintering areas to prepare for migration by feeding extensively and, in some cases, molting their flight feathers. These additional migrations may play a key role in population structure, but are not included in traditional models of migratory connectivity. Network analysis, which applies graph theory to assess linkages between discrete locations or entities, offers a powerful tool for quantitatively assessing the contributions of different sites used throughout the annual cycle to complex spatial networks. We collected satellite telemetry data on annual cycle movements of 672 individual sea ducks of five species from throughout eastern North America and the Great Lakes. From these data, we constructed a multi‐species network model of migratory patterns and site use over the course of breeding, molting, wintering, and migratory staging. Our results highlight inter‐ and intra‐specific differences in the patterns and complexity of annual cycle movement patterns, including the central importance of staging and molting sites in James Bay, the St. Lawrence River, and southern New England to multi‐species annual cycle habitat linkages, and highlight the value of Long‐tailed Ducks ( Calengula haemalis ) as an umbrella species to represent the movement patterns of multiple sea duck species. We also discuss potential applications of network migration models to conservation prioritization, identification of population units, and integrating different data streams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Network analysis of memristive device circuits: dynamics, stability and correlations

Abstract Networks with memristive devices are a potential basis for the next generation of computing devices. They are also an important model system for basic science, from modeling nanoscale conductivity to providing insight into the information-processing of neurons. The resistance in a memristive device depends on the history of the applied bias and thus displays a type of memory. The interplay of this memory with the dynamic properties of the network can give rise to new behavior, offering many fascinating theoretical challenges. But methods to analyze general memristive circuits are not well described in the literature. In this paper we develop a general circuit analysis for networks that combine memristive devices alongside resistors, capacitors and inductors and under various types of control. We derive equations of motion for the memory parameters of these circuits and describe the conditions for which a network should display properties characteristic of a resonator system. For the case of a purely memresistive network, we derive Lyapunov functions, which can be used to study the stability of the network dynamics. Surprisingly, analysis of the Lyapunov functions show that these circuits do not always have a stable equilibrium in the case of nonlinear resistance and window functions. The Lyapunov function allows us to study circuit invariances, wherein different circuits give rise to similar equations of motion, which manifest through a gauge freedom and node permutations. Finally, we identify the relation between the graph Laplacian and the operators governing the dynamics of memristor networks operators, and we use these tools to study the correlations between distant memristive devices through the effective resistance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Incorporating machine learning with building network analysis to predict multi-building energy use

Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a ‘‘pan-cell-state’’ strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Compilation and utilization of a sorghum transcriptome compendium for gene regulatory network analysis and crop trait engineering

Sorghum bicolor (Sorghum) is a drought and heat tolerant C4 grass crop used to produce grain, forage, biofuels, and other bioproducts. Genetic improvement of sorghum hybrid crops is aided by a large and diverse germplasm, sorghum's diploid inbreeding genetics, and a relatively small genome that has facilitated genomic research. Over the past 20 years, the sorghum research community characterized the cytogenetic and recombinant landscapes of sorghum's 10 chromosomes, sequenced and annotated the sorghum genome, and used that information to identify genes/alleles that modulate flowering time, plant height, seed shattering, and other important traits. More recently, >1000 RNA-seq transcriptome profiles were collected from 15 sorghum genotypes to help understand the genetic basis of variation in growth and development of sorghum stems, tillers, roots, and leaves, and the regulation of biosynthetic pathways that produce epicuticular wax, dhurrin, and RFOs, compounds that contribute to sorghum's resilience. Transcriptome studies were designed to identify differentially expressed genes that are co-expressed during development or in response to a treatment to enable construction of gene regulatory networks. Co-expression and network analysis identified transcription factors and their cognate binding sites in target gene promoters and signaling pathways that modulate gene regulatory networks providing gene editing targets for further trait optimization. RNA-seq data from >20 experiments targeting sorghum organs, tissues, cell types, developmental stages, and responses to environmental conditions (i.e., diel, day-length, shading, water-deficit, temperature) has been compiled in a sorghum transcriptome compendium. The goal of this resource paper is to describe compendium content, accessibility, and a compendium data analysis pipeline and to illustrate the types of information that can be derived from the compendium with a focus on the elucidation of gene regulatory networks useful for guiding the improvement of sorghum traits through gene editing.

RNA-seq↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Bioenergy sorghum stem growth regulation: intercalary meristem localization, development, and gene regulatory network analysis

SUMMARY Bioenergy sorghum is a highly productive drought tolerant C 4 grass that accumulates 80% of its harvestable biomass in approximately 4 m length stems. Stem internode growth is regulated by development, shading, and hormones that modulate cell proliferation in intercalary meristems (IMs). In this study, sorghum stem IMs were localized above the pulvinus at the base of elongating internodes using magnetic resonance imaging, microscopy, and transcriptome analysis. A change in cell morphology/organization occurred at the junction between the pulvinus and internode where LATERAL ORGAN BOUNDARIES ( SbLOB ), a boundary layer gene, was expressed. Inactivation of an AGCVIII kinase in DDYM ( dw2 ) resulted in decreased SbLOB expression, disrupted IM localization, and reduced internode cell proliferation. Transcriptome analysis identified approximately 1000 genes involved in cell proliferation, hormone signaling, and other functions selectively upregulated in the IM compared with a non‐meristematic stem tissue. This cohort of genes is expressed in apical dome stem tissues before localization of the IM at the base of elongating internodes. Gene regulatory network analysis identified connections between genes involved in hormone signaling and cell proliferation. The results indicate that gibberellic acid induces accumulation of growth regulatory factors (GRFs) known to interact with ANGUSTIFOLIA (SbAN3), a master regulator of cell proliferation. GRF:AN3 was predicted to induce SbARF3/ETT expression and regulate SbAN3 expression in an auxin‐dependent manner. GRFs and ARFs regulate genes involved in cytokinin and brassinosteroid signaling and cell proliferation. The results provide a molecular framework for understanding how hormone signaling regulates the expression of genes involved in cell proliferation in the stem IM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Bacterial Community Shift and Coexisting/Coexcluding Patterns Revealed by Network Analysis in a Uranium-Contaminated Site after Bioreduction Followed by Reoxidation

A site in Oak Ridge, TN, USA, has sediments that contain >3% iron oxides and is contaminated with uranium (U). The U(VI) was bioreduced to U(IV) and immobilized in situ through intermittent injections of ethanol. It then was allowed to reoxidize via the invasion of low-pH (3.6 to 4.0), high-nitrate (up to 200 mM) groundwater back into the reduced zone for 1,383 days. To examine the biogeochemical response, high-throughput sequencing and network analysis were applied to characterize bacterial population shifts, as well as cooccurrence and coexclusion patterns among microbial communities. A paired t test indicated no significant changes of α-diversity for the bioactive wells. However, both nonmetric multidimensional scaling and analysis of similarity confirmed a significant distinction in the overall composition of the bacterial communities between the bioreduced and the reoxidized sediments. The top 20 major genera accounted for >70% of the cumulative contribution to the dissimilarity in the bacterial communities before and after the groundwater invasion. Castellaniella had the largest dissimilarity contribution (17.7%). For the bioactive wells, the abundance of the U(VI)-reducing genera Geothrix, Desulfovibrio, Ferribacterium, and Geobacter decreased significantly, whereas the denitrifying Acidovorax abundance increased significantly after groundwater invasion. Additionally, seven genera, i.e., Castellaniella, Ignavibacterium, Simplicispira, Rhizomicrobium, Acidobacteria Gp1, Acidobacteria Gp14, and Acidobacteria Gp23, were significant indicators of bioactive wells in the reoxidation stage. Canonical correspondence analysis indicated that nitrate, manganese, and pH affected mostly the U(VI)-reducing genera and indicator genera. Cooccurrence patterns among microbial taxa suggested the presence of taxa sharing similar ecological niches or mutualism/commensalism/synergism interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Neural Network Analysis on the NOvA Experiment

NOνA is collaboration of 180 scientists and engineers from 28 institutions which plans to study neutrino oscillations using the existing NuMI neutrino beam at Fermilab. The NOνA experiment is designed to search for oscillations of muon neutrinos to electron neutrinos by comparing the electron neutrino event rate measured at the Fermilab site with the electron neutrino event rate measured at a location just south of International Falls, MN 810 kilometers distant from Fermilab. If oscillations occur, the far site will see the appearance of electrons in the muon neutrino beam produced at Fermilab. The presence of background radiation obscures the desired particles and trails to be observed. Using neural network analysis, the goal of the project was to implement machine learning to automate the removal of background radiation to render pixel maps of the particle trajectories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Weighted gene co-expression network analysis reveals potential genes involved in early metamorphosis process in sea cucumber Apostichopus japonicus

Highlights: • Gene expression profiles of sea cucumber were systematically examined. • Gene co-expression modules of sea cucumber were analyzed for the first time. • The module MEdarkmagenta is highly related to the early metamorphosis process. • Genes associated with the early metamorphosis process were identified. Sea cucumbers, one main class of Echinoderms, have a very fast and drastic metamorphosis process during their development. However, the molecular basis under this process remains largely unknown. Here we systematically examined the gene expression profiles of Japanese common sea cucumber (Apostichopus japonicus) for the first time by RNA sequencing across 16 developmental time points from fertilized egg to juvenile stage. Based on the weighted gene co-expression network analysis (WGCNA), we identified 21 modules. Among them, MEdarkmagenta was highly expressed and correlated with the early metamorphosis process from late auricularia to doliolaria larva. Furthermore, gene enrichment and differentially expressed gene analysis identified several genes in the module that may play key roles in the metamorphosis process. Our results not only provide a molecular basis for experimentally studying the development and morphological complexity of sea cucumber, but also lay a foundation for improving its emergence rate.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs

Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Cross-boundary collaboration in waste management research: A network analysis

Highlights: • No core scholars stood out from cross-boundary research collaboration networks. • No institution occupied a critical position. • Old-line developed countries stay in the center of research than developing ones. • More research has focused on “hard” technologies than “soft” measures. • CBRC from different perspectives were limited but increasing steadily over time. - Abstract: This paper aims to illustrate the cross-boundary research collaboration (CBRC) landscape of waste management (WM) by various collaboration networks. Through a set of rigorous procedures, a total of 15,396 research papers were extracted from eight subject-related journals published between 1981 and 2016. The authors utilized CiteSpace, a Java programme that helps visualize and dissect patterns in scientific literature, to evaluate the content through individual, institutional, national, and disciplinary perspectives. The evaluations of three former perspectives revealed a steady rise in CBRC within WM over the last thirty-five years, although the overall intensities proved fairly low. Inter-individual collaboration groups were limited to their respective regions and only loosely connected, but as more and more academic institutions and universities engaged in WM research, the number and quality of the collaborations increased. Developed countries, chiefly in North America and Western Europe, comprised the bulk of the WM research, whilst the mounting contributions from developing countries, China in particular, forecasts greater diversity in the future. The analysis also suggested that the intensity of the interdisciplinary collaboration network declined slightly, however, the intensity proved low to begin with. Previous WM research focused more on “hard” technologies than “soft” measures. Future endeavors to encourage CBRC in WM should promote more innovative research to tackle waste challenges globally in a sustainable way.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Gene network analysis of poplar root transcriptome in response to drought stress identifies a PtaJAZ3PtaRAP2.6-centered hierarchical network

Using time-series transcriptomic data from poplar roots undergoing polyethylene glycol (PEG)-induced drought stress, we built a genetic network model of the involved putative molecular responses. We found that the network resembled a hierarchical structure. The highest hierarchical level in this structure is occupied by 9 genes, which we called superhubs because they were primarily connected to 18 hub genes, which are then connected to 2,934 terminal genes. We were only able to regenerate transgenic plants overexpressing two of the superhubs, suggesting that the majority of the superhubs might interfere with the regeneration process and did not allow recovery of transgenic plants. The two superhubs encode proteins with closest homology to JAZ3 and RAP2.6 genes of Arabidopsis and were consequently named PtaJAZ3 and PtaRAP2.6. PtaJAZ3 and PtaRAP2.6 overexpressing transgenic lines showed a significant increase in both root elongation and lateral root proliferation and these responses were specific for the drought stress conditions and were highly correlated with the levels of overexpression of the transgenes. Several lines of evidence suggest of regulatory interactions between the two superhubs. Both superhubs were significantly induced by methyl jasmonate (MeJA). Because jasmonate signaling involves ubiquitin-mediated proteasome degradation, treatment with proteasome inhibitor abolished the MeJA induction for both genes. PtaRAP2.6 was upregulated in PtaJAZ3 transgenics but PtaJAZ3 expression was not affected in the PtaRAP2.6 overexpressors. The discovery of the two genes and further future insights into the associated mechanisms can lead to improved understanding and novel approaches to regulate root architecture in relation to drought stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients. Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods. Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery. Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention. Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗