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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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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Emissions of biogenic volatile organic compounds from arctic shrub litter are coupled with changes in the bacterial community composition

Emissions of biogenic volatile organic compounds (BVOCs) from natural ecosystems impact atmospheric chemistry as well as biological interactions and even soil biogeochemical processes. Plant litter emits substantial amounts of BVOCs. These emissions may contribute to total ecosystem emissions especially in the Arctic where the living plant biomass is low and the amount of litter is expected to increase as the deciduous shrubs expand in response to a warmer climate. Here, we incubated in the laboratory litter from the evergreen Cassiope tetragona and deciduous Salix spp. from a high arctic and a low arctic location. The 8-week-long incubation was conducted with temperature increasing from 5 °C to 26 °C, mimicking the transition from winter to summer. BVOC emissions from the decomposing litter were sampled weekly in adsorbent cartridges and analyzed using gas chromatography–mass spectrometry, and the bacterial community composition was investigated by sequencing of PCR amplified 16S rRNA gene fragments. Our results showed that litter from C. tetragona, which is a terpenoid storing species, had higher BVOC emission rates (mainly terpenoids) than the Salix litter, which does not have specialized BVOC storing compartments. The C. tetragona litter emissions were higher in the high arctic than the low arctic samples. The emission rates from the C. tetragona litter increased during the incubation period, whereas emission rates from the Salix litter decreased, suggesting that the emissions originated from different sources and/or processes. The bacterial community composition in the Salix litter, but not in the C. tetragona litter, changed in parallel with the changes in the BVOC emissions during the incubation period. Therefore, we suggest that bacteria may be more important for the BVOC emissions from decomposing Salix litter than C. tetragona litter.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Complex decay dynamics of HIV virions, intact and defective proviruses, and 2LTR circles following initiation of antiretroviral therapy

In persons living with HIV-1 (PLWH) who start antiretroviral therapy (ART), plasma virus decays in a biphasic fashion to below the detection limit. The first phase reflects the short half-life (<1 d) of cells that produce most of the plasma virus. The second phase represents the slower turnover (t 1/2 = 14 d) of another infected cell population, whose identity is unclear. Using the intact proviral DNA assay (IPDA) to distinguish intact and defective proviruses, we analyzed viral decay in 17 PLWH initiating ART. Circulating CD4 + T cells with intact proviruses include few of the rapidly decaying first-phase cells. Instead, this population initially decays more slowly (t 1/2 = 12.9 d) in a process that largely represents death or exit from the circulation rather than transition to latency. This more protracted decay potentially allows for immune selection. After ~3 mo, the decay slope changes, and CD4 + T cells with intact proviruses decay with a half-life of 19 mo, which is still shorter than that of the latently infected cells that persist on long-term ART. Two-long-terminal repeat (2LTR) circles decay with fast and slow phases paralleling intact proviruses, a finding that precludes their use as a simple marker of ongoing viral replication. Proviruses with defects at the 5' or 3' end of the genome show equivalent monophasic decay at rates that vary among individuals. Understanding these complex early decay processes is important for correct use of reservoir assays and may provide insights into properties of surviving cells that can constitute the stable latent reservoir.

2LTR circles↗
DOE OSTI2023

Nitrogen fixation rates in the Guinea Dome and the equatorial upwelling regions in the Atlantic Ocean

Abstract Biological nitrogen fixation is a key process balancing the loss of combined nitrogen in the marine nitrogen cycle. Its relevance in upwelling or high nutrient regions is still unclear, with the few available studies in these regions of the ocean reporting rates that vary widely from below detection limit to > 100 nmol N L −1 d −1 . In the eastern tropical Atlantic Ocean, two open ocean upwelling systems are active in boreal summer. One is the seasonal equatorial upwelling, where the residual phosphorus associated with aged upwelled waters is suggested to enhance nitrogen fixation in this season. The other is the Guinea Dome, a thermal upwelling dome. We conducted two surveys along 23° W across the Guinea Dome and the Equator from 15° N to 5° S in September 2015 and August–September 2016 with high latitudinal resolution (20–60 nm between stations). The abundance of Trichodesmium colonies was characterized by an Underwater Vision Profiler 5 and the total biological nitrogen fixation in the euphotic layer was measured using the 15 N 2 technique. The highest abundances of Trichodesmium colonies were found in the area of the Guinea Dome (9°–15° N) with a maximum of 3 colonies L −1 near the surface. By contrast, colonies were almost absent in the Equatorial band between 2° N and 5° S. The highest nitrogen fixation rate was measured at the northern edge of the Guinea Dome in 2016 (ca. 31 nmol N L −1 d −1 ). In this region, where diazotrophs thrived on a sufficient supply of both phosphorus and iron, a patchy distribution was unveiled by our increased spatial resolution scheme. In the Equatorial band, rates were considerably lower, ranging from below detection limit to ca. 4 nmol N L −1 d −1 , with a clear difference in magnitude between 2015 (rates close to zero) and 2016 (average rates around 2 nmol N L −1 d −1 ). This difference seemed triggered by a contrasting supply of phosphorus between years. Our study stresses the importance of surveys with sampling at fine-scale spatial resolution, and shows unexpected high variability in the rates of nitrogen fixation in the Guinea Dome, a region where diazotrophy is a significant process supplying new nitrogen into the euphotic layer.

Fernández-Carrera, Ana (ORCID:0000000183470996)↗
DOE OSTI2025

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗
NASA NTRS1992

Toward a microgravity research strategy

Recommendations of the Committee on Microgravity Research (CMGR) of the Space Studies Board of the National Research Council are found in the Summary and Recommendations in the front of the report. The CMGR recommends a long-range research strategy. The main rationale for the microgravity research program should be to improve our fundamental scientific and technical knowledge base, particularly in the areas that are likely to lead to improvements in processing and manufacturing on earth. The CMGR recommends research be categorized as Biological science and technology, Combustion, Fluid science, Fundamental phenomena, Materials, and Processing science and technology. The committee also recommends that NASA apply a set of value criteria and measurement indicators to define the research and analysis program more clearly. The CMGR recommends that the funding level for research and analysis in microgravity science be established as a fixed percentage of the total program of NASA's Microgravity Science and Applications Division in order to build a strong scientific base for future experiments. The committee also recommends a cost-effective approach to experiments. Finally the CMGR recommends that a thorough technical review of the centers for commercial development of space be conducted to determine the quality of their activities and to ascertain to what degree their original mission has been accomplished.

Source record↗
NASA NTRS2003

Autotrophic Ecosystems on the Early Earth

Ophiolite sequences, sections of lower oceanic crust and upper mantle that have been thrust onto continental craton, are located in northern and central California and provide easily accessible areas that serve as good analogs for similar, more extensive areas of the early Earth. We have begun investigating and characterizing these sites in order to understand better the processes that may be responsible for the water chemistry, mineralogy and biology of similar environments on the early Earth. The geophysical and geochemical processes in these terranes provide niches for unique communities of extremeophiles and likely provide a good analog to the location that first gave rise to life on Earth. The ophiolites found in northern and central California include the Trinity, Josephine, Coast Range and Point Sal, all of which are approximately 160 million years old. Fluids from serpentinizing springs are generally alkaline with high pH and H2 contents, indicating that the mafic rock compositions control the fluid composition through water-rock reactions during relatively low-grade hydrothermal processes. There are significant amounts of primary mineralogy remaining in the rocks, meaning that substantial alteration processes are still occurring in these terranes. The general reaction for serpentinization of olivine is given by one of the authors. olivine + H2O = serpentine + brucite + magnetite + H2. We have analyzed the mineralogical composition of several rock samples collected from the Coast Range Ophiolite near Clear Lake, CA by electron microprobe. The remnant primary mineralogy is fairly urnform in composition, with an olivine composition of Fo(sub 90), and with pyroxene compositions of En(sub 90) for orthopyroxene and En(sub 49)Wo(sub 48)Fs(sub 03) for the clinopyroxene. Other primary phases observed include chromites and other spinels. Examination of petrographic thin sections reveals that serpentinization reactions have occurred in these locations. The serpentine resulting from aqueous alteration of olivine resides in veins that are see to cross cut the primary mineral grains. There are several generations of alteration products, comprised mostly of serpentines that are magnesium rich, with magnetite, brucite and carbonates observed as accessory minerals. The formation of carbonates can be taken to indicate the presence of CO2 in the altering fluids. We collected samples from a spring in the Coast Range Ophiolite in order to determine whether the geochemical environment serves as a habitat for chemotrophic microorganisms. DNA was extracted from the sediment samples and the 16s rRNA gene was PCR amplified using universal Archaeal primers. Denaturing gradient gel electrophoresis (DGGE) was used to determine the community of Archaea thriving in these samples. Our results indicate that there were 8 different genera of Archaea from a single sample. A sequence was obtained from one of these eight. The sequence is of an organism similar to Halorubrum tibetense, and alkalophilic Archaeon. This result suggests that these environments are likely hosts for communities of organisms that are adapted for the unique chemistry provided by the alkaline spring.

Schulte, M.↗
NASA NTRS2009

Evolutionary Computing Methods for Spectral Retrieval

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Terrile, Richard↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2021

Nanoscale biochemical sample preparation and analysis

Provided herein are methods and systems for biochemical analysis, including compositions and methods for processing and analysis of small cell populations and biological samples (e.g., a robotically controlled chip-based nanodroplet platform). In particular aspects, the methods described herein can reduce total processing volumes from conventional volumes to nanoliter volumes within a single reactor vessel (e.g., within a single droplet reactor) while minimizing losses, such as due to sample evaporation.

Kelly, Ryan T.↗
DOE OSTI2022

Exploring New Ways to Classify Industries for Energy Analysis and Modeling

As the US moves closer to embracing a net zero greenhouse gas emissions position, combustion processes outside the power sector are becoming urgent concerns. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning that even a single product group can vary broadly in its production routes and associated energy usage. In the US, the North American Industry Classification System (NAICS) serves as the basis for data collection and reporting. In turn, data based on NAICS is the foundation of most US energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for plans to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data to build more detail into heterogeneous sectors is scarce. This work explores alternative classification schemes for industry based on energy use characteristics, and provides a validation of an approach to make facility-level energy use estimates based on publicly available data from the greenhouse gas reporting program. First, several approaches to industrial taxonomies and their usefulness for industrial energy modeling are summarized. Data from Industrial Assessment Centers is analyzed using unsupervised machine learning techniques to detect clusters. Cladistics, an approach from biology, is adapted to energy and process characteristics of industries. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation. Finally, validation is performed for facility-level energy estimates from the US EPA Greenhouse Gas Reporting Program. This validation assists in making this data source available for use in energy modeling. Together, this work explores alternative approaches for categorizing industries in a way that aids understanding energy use, and presenting pathways for the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications

Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving bioeconomic growth. Trichoderma species are well-studied model fungal organisms used for their biocontrol properties with great potential to alleviate the use of agrochemicals in agriculture. However, identifying and characterizing effective natural products in novel species or strains as biological control products remains a meticulous process with many known challenges to be navigated. Integration of recent advancements in various “omics” technologies, next generation biodesign, machine learning, and artificial intelligence approaches could greatly advance bioprospecting goals. Herein, we propose a roadmap for assessing the potential impact of already known or newly discovered Trichoderma species for biocontrol applications. By screening publicly available Trichoderma genome sequences, we first highlight the prevalence of putative biosynthetic gene clusters and antimicrobial peptides among genomes as an initial step toward predicting which organisms could increase the diversity of natural products. Next, we discuss high-throughput methods for screening organisms to discover and characterize natural products and how these findings impact both fundamental and applied research fields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Quantitative soil zymography: Mechanisms, processes of substrate and enzyme diffusion in porous media

Soil membrane zymography enables 2D mapping of enzyme activities on the surface of soil samples. The method is according to diffusion of components of enzymatically-mediated reactions to/from membrane, and, hence, reflects the distribution of enzyme activities at the intact soil surface. Zymography has been already successfully implemented in numerous soil ecology applications. Here we identify two methodological aspects for further improvement and expansion of the method at micro and macro scales: first, accounting for the area of contact between the soil surface and the zymography membranes and, second, accounting for diffusion effects during the zymography procedure. We tested three methods, namely, laser-scanning, staining with a fluorescent product (e.g. MUF: 4-methylumbelliferone), and X-ray computed micro-tomography, for assessing the area of the soil surface in contact with the membranes. We quantified diffusion of MUF, enzymes and substrate between the substrate-saturated membrane and soil as well as diffusion processes during membrane zymography via HP2 software. Diffusion of the substrate from the membrane and of the MUF-product to the membrane was detected, while there were no clear evidence of enzyme diffusion to/in the membrane. According to the model simulations, the enzyme activities detected via 2D zymography probably represent only a small portion, about 20%, of the actual reactions within the soil volume that is in both direct contact and in hydrological contact with zymography membranes. This is a result of omnidirectional diffusion of reaction products. The membrane contact with the soil surface estimated by three methods ranged from 3.4 to 36.5% further signifying that only a fraction of enzymes activity is detectable in a course of 2D soil zymography.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Elviz – exploration of metagenome assemblies with an interactive visualization tool

Metagenomics, the sequencing of DNA collected from an entire microbial community, enables the study of natural microbial consortia in their native habitats. Metagenomics studies produce huge volumes of data, including both the sequences themselves and metadata describing their abundance, assembly, predicted functional characteristics and environmental parameters. The ability to explore these data visually is critically important to meaningful biological interpretation. Current genomics applications cannot effectively integrate sequence data, assembly metadata, and annotation to support both genome and community-level inquiry. Elviz (Environmental Laboratory Visualization) is an interactive web-based tool for the visual exploration of assembled metagenomes and their complex metadata. Elviz allows scientists to navigate metagenome assemblies across multiple dimensions and scales, plotting parameters such as GC content, relative abundance, phylogenetic affiliation and assembled contig length. Furthermore Elviz enables interactive exploration using real-time plot navigation, search, filters, axis selection, and the ability to drill from a whole-community profile down to individual gene annotations. Thus scientists engage in a rapid feedback loop of visual pattern identification, hypothesis generation, and hypothesis testing. Compared to the current alternative of generating a succession of static figures, Elviz can greatly accelerate the speed of metagenome analysis. Elviz can be used to explore both user-submitted datasets and numerous metagenome studies publicly available at the Joint Genome Institute (JGI). Elviz is freely available at http://genome.jgi.doe.gov/viz and runs on most current web-browsers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Omics-Lethal Human Viruses, West Nile Experiment WCN002

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus (WNV NY99 clone 382) and mutant (WNVE218A) virus infection. Sample data was obtained from mouse cortical neuron tissue and processed for mRNA and miRNA expression analysis. This data set comprises two complete biological replicate experiments conducted in the same conditions and with data processed independently (WCN003). Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset downloads have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges

Abstract Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Biochemistry & Molecular Biology↗
DOE OSTI2014

A First-Passage Kinetic Monte Carlo method for reaction–drift–diffusion processes

Stochastic reaction–diffusion models are now a popular tool for studying physical systems in which both the explicit diffusion of molecules and noise in the chemical reaction process play important roles. The Smoluchowski diffusion-limited reaction model (SDLR) is one of several that have been used to study biological systems. Exact realizations of the underlying stochastic processes described by the SDLR model can be generated by the recently proposed First-Passage Kinetic Monte Carlo (FPKMC) method. This exactness relies on sampling analytical solutions to one and two-body diffusion equations in simplified protective domains. In this work we extend the FPKMC to allow for drift arising from fixed, background potentials. As the corresponding Fokker–Planck equations that describe the motion of each molecule can no longer be solved analytically, we develop a hybrid method that discretizes the protective domains. The discretization is chosen so that the drift–diffusion of each molecule within its protective domain is approximated by a continuous-time random walk on a lattice. New lattices are defined dynamically as the protective domains are updated, hence we will refer to our method as Dynamic Lattice FPKMC or DL-FPKMC. We focus primarily on the one-dimensional case in this manuscript, and demonstrate the numerical convergence and accuracy of our method in this case for both smooth and discontinuous potentials. We also present applications of our method, which illustrate the impact of drift on reaction kinetics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2002

Radiation Effect on Human Tissue

Predicting the occurrence of human cancer following exposure of an epidemiologic population to any agent causing genetic damage is a difficult task. To an approximation, this is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within clinically normal individuals. This situation begs the need for alternate controlled experimental models that are predictive for the development of human cancer following exposures to agents causing genetic damage. Such models historically have not been of substantial proven value. It is more recently encouraging, however, that developments in molecular and cell biology have led to an expanded knowledge of human carcinogenesis, and of molecular markers associated with that process. It is therefore appropriate to consider new laboratory models developed to accomodate that expanded knowledge in order to assess the cancer risks associated with exposures to genotoxic agents. When ionizing radiation of space is the genotoxic agent, then a series of additional considerations for human cancer risk assessment must also be applied. These include the dose of radiation absorbed by tissue at different locations in the body, the quality of the absorbed radiation, the rate at which absorbed dose accumulates in tissue, the way in which absorbed dose is measured and calculated, and the alterations in incident radiation caused by shielding materials. It is clear that human cancer risk assessment for damage caused by ionizing radiation is a multidisciplinary responsibility, and that within this responsibility no single discipline can hold disproportionate sway if a risk assessment model of radiation-induced human cancer is to be developed that has proven value. Biomolecular and cellular markers from the work reported here are considered for use in assessing human cancer risk related to exposure to space radiation. This potential use must be integrated within the specified multidisciplinary context in order to create a new tool of molecular epidemiology that can hopefully then realistically assess this cancer risk.

Richmond, Robert C.↗
DOE OSTI2015

Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

Objective: Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury. Methods: Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances. Results: 99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers. Conclusion: We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.

59 BASIC BIOLOGICAL SCIENCES↗