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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 OSTI2021

LncRNA embryonic stem cells expressed 1 (Lncenc1) is identified as a novel regulator in neuropathic pain by interacting with EZH2 and downregulating the expression of Bai1 in mouse microglia

LncRNA embryonic stem cells expressed 1 (Lncenc1), named after its high expression in naïve embryonic stem cells (nESCs), has been rarely studied in almost all pathological processes. Evidences suggest that Lncenc1 is likely to work in the form of RNA-protein complex. Here, we found that Lncenc1 in dorsal root ganglion (DRG) was significantly upregulated in response to mouse nerve injury caused by partial sciatic nerve ligation (pSNL). Overexpression of Lncenc1 mediated by adenoviral expression vector promoted the activation of microglia and the production of inflammatory cytokines including TNF-α, IL-1β and MCP-1. In contrast, knockdown of Lncenc1 suppressed activation of microglia and production of inflammatory cytokines. In the mechanism exploration, we found that Lncenc1 could bind with the RNA binding protein (RBP) enhancer of zeste homologue 2 (EZH2), an identified contributor in microglial activation and neuropathic pain. Lncenc1 interacted with EZH2 and downregulated the expression of brain-specific angiogenesis inhibitor 1 (BAI1). Either inhibition of EZH2 or overexpression of BAI1 could reverse the effects of Lncenc1 overexpression on microglial activation and neuroinflammation. Finally, the Lncenc1-siRNA was intrathecally injected into pSNL mice, and its effects on neuropathic pain were evaluated. Knockdown of Lncenc1 attenuated the development and maintenance of mechanical and thermal hyperalgesia of pSNL mice, accompanied by an increase in BAI1 expression and decrease in inflammatory cytokines. In conclusion, Lncenc1 contributes to neuropathic pain by interacting with EZH2 and downregulating the BAI1 gene in mouse microglia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Heavy Ion Minibeam Therapy: Side Effects in Normal Brain

The purpose of this work was to investigate whether minibeam therapy with heavy ions might offer improvements of the therapeutic ratio for the treatment of human brain cancers. To assess neurotoxicity, we irradiated normal juvenile rats using 120 MeV lithium-7 ions at an absorbed integral dose of 20 Gy. Beams were configured either as a solid parallel circular beam or as an array of planar parallel minibeams having 300-micron width and 1-mm center-to-center spacing within a circular array. We followed animals for 6 months after treatment and utilized behavioral testing and immunohistochemical studies to investigate the resulting cognitive impairment and chronic pathologic changes. We found both solid-beam therapy and minibeam therapy to result in cognitive impairment compared with sham controls, with no apparent reduction in neurotoxicity using heavy ion minibeams instead of solid beams under the conditions of this study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

GLIDR promotes the progression of glioma by regulating the miR-4677-3p/MAGI2 axis

Gliomas are the most common and fatal primary brain tumors. Growing evidence suggests that long non-coding RNAs (lncRNAs) constitute novel and potential therapeutic targets for glioma. However, the biological role of glioblastoma down-regulated RNA (GLIDR) in glioma remains largely elusive. In the current study, we used quantitative real-time polymerase chain reaction (qRT-PCR) to detect GLIDR expression in glioma cells. Cell counting kit 8 (CCK-8) assay, colony formation assay, JC-1 staining, and flow cytometry were used to evaluate the role of GLIDR in proliferation and apoptosis of glioma cells. Western blotting was performed to assess the effect of GLIDR on the level of apoptosis-related proteins. In addition, bioinformatics prediction, RNA immunoprecipitation (RIP), RNA pull-down, and luciferase reporter gene assays were used to study the regulatory mechanisms of GLIDR in glioma. GLIDR was found to be highly expressed in glioma cells and silencing of GLIDR inhibited cell proliferation and promoted apoptosis. Functionally, GLIDR bound to miR-4677-3p that directly targeted membrane-associated guanylate kinase, WW, and PDZ domain-containing protein 2 (MAGI2). Our data showed that GLIDR affects the proliferation and apoptosis of glioma cells by targeting miR-4677-3p to regulate the expression of MAGI2. In conclusion, our study determined the oncogenic role of GLIDR in glioma, which may provide a new perspective for the treatment of glioma.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Efficient human activity recognition with spatio-temporal spiking neural networks

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Culturing astrocytes on substrates that mimic brain tumors promotes enhanced mechanical forces

Highlights: • Human astrocytes increase traction forces of cells applied to the ECM in response to increased substrate stiffness. • Astrocytes apply intercellular stresses with noticeable increase in higher substrate stiffness. • Cell velocity decreases in human astrocytes in brain tumor like environment compared to normal brain environment. • Astrocytes mechanical strength and function could be potentially enhanced by increased stiffness of the microenvironment. Astrocytes are essential to brain homeostasis and their dysfunction can have devastating consequences on human quality of life. Such deleterious effects are generally due in part to changes that occur at the cellular level, which may be biochemical or biomechanical in nature. One biomechanical change that can occur is a change in tissue stiffness. Brain tumors are generally associated with increased brain tissue stiffness, but the impact increased tissue stiffness has on astrocyte biomechanical behavior is poorly understood. Therefore, in this study we cultured human astrocytes on flexible substrates with stiffness that mimicked the healthy human brain (1 kPa), meningioma (4 kPa), and glioma (11 kPa) and investigated astrocyte biomechanical behavior by measuring cell-substrate tractions, strain energies, cell-cell intercellular stresses, and cellular velocities. In general, tractions, intercellular stresses, and strain energy was observed to increase as a function of increased substrate stiffness, while cell velocities were observed to decrease with increased substrate stiffness. We believe this study will be of great importance to the fields of brain pathology and brain physiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning

Here we present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to compress the information contained within each partition. We train both of these components simultaneously by optimizing a loss function that is designed to promote accurate reconstruction of each partition, while encouraging spatially smooth and contiguous partitioning, and discouraging relatively small partitions. We show that the partitions adapt to the subject specific structural variations of brain tissue while consistently appearing at similar spatial locations across subjects. AutoAtlas also produces very low dimensional features that represent local texture of each partition. We demonstrate prediction of metadata associated with each subject using the derived feature representations and compare the results to prediction using features derived from FreeSurfer anatomical parcellation. Since our features are intrinsically linked to distinct partitions, we can then map values of interest, such as partition-specific feature importance scores onto the brain for visualization.

42 ENGINEERING↗
NASA NTRS1998

Investigation of EM Emissions by the Electrodynamic Tether, Inclusive of an Observational Program (EMET)

Our TSS-1/R investigation, which we shall refer to as EMET in this report, was an integral part of the effort by the TSS-1/R Investigators' Working Group (IWG) to come to an understanding of the complex interaction between the tethered satellite system and the ionosphere. All of the space-borne experiments were designed to collect data relevant to the local interaction. Only the ground- based experiments, EMET and its Italian counterpart Observations on the Earth's Surface of Electromagnetic Emissions (OESEE), held out any hope of characterizing the long range effects of the interaction. This was to be done by detecting electromagnetic waves generated by the system in the ionosphere, assuming the signal reached the Earth's surface with sufficient amplitude. As the type of plasma waves excited to carry charge away from the charge-exchange regions of the system at each end of the tether is one of the theoretical points about which there is greatest disagreement, a definitive identification of tether-generated waves could mark significant progress in the so-called current closure problem of electrodynamic tethers. Dr. Mario Grossi of the Smithsonian Astrophysical Observatory (SAO) initiated the investigation, and his experience in the field of ULF-ELF waves and their detection was invaluable throughout its course. Rice University had the responsibility of setting up the EMET ULF-VLF ground stations under a subcontract from SAO. Principal Investigator (PI) for the Rice effort was Prof. William E. Gordon, who was primary observer at the Arecibo Observatory during TSS-LR. Dr. Steve Noble handled major day-to-day operations, training, and planning for the ground-based measurements. Dr. James McCoy of NASA JSC, a member of the Mona/Arecibo team, was pilot for the numerous flights ferrying personnel and equipment between Puerto Rico and Mona Island. Final responsibility for the measurements rested with SAO, and the activities of field personnel and SAO investigators were closely co-ordinated during the mission. Dr. Enrico Lorenzini of SAO served as the eyes, ears, and brain of EMET in the Science Operations Area and PI table during the mission, whenever the PI was absent during the round-the-clock mission operations. The Rice University final report to SAO, which is included as an Appendix, contains details of the remote sites, means of communication, sensors, etc., as well as the affiliations of personnel involved in the data-gathering effort.

Estes, Robert D.↗
NASA NTRS2001

Adaptive Instrument Module: Space Instrument Controller "Brain" through Programmable Logic Devices

The Adaptive Instrument Module (AIM) will be the first true demonstration of reconfigurable computing with field-programmable gate arrays (FPGAs) in space, enabling the 'brain' of the system to evolve or adapt to changing requirements. In partnership with NASA Goddard Space Flight Center and the Australian Cooperative Research Centre for Satellite Systems (CRC-SS), APL has built the flight version to be flown on the Australian university-class satellite FEDSAT. The AIM provides satellites the flexibility to adapt to changing mission requirements by reconfiguring standardized processing hardware rather than incurring the large costs associated with new builds. This ability to reconfigure the processing in response to changing mission needs leads to true evolveable computing, wherein the instrument 'brain' can learn from new science data in order to perform state-of-the-art data processing. The development of the AIM is significant in its enormous potential to reduce total life-cycle costs for future space exploration missions. The advent of RAM-based FPGAs whose configuration can be changed at any time has enabled the development of the AIM for processing tasks that could not be performed in software. The use of the AIM enables reconfiguration of the FPGA circuitry while the spacecraft is in flight, with many accompanying advantages. The AIM demonstrates the practicalities of using reconfigurable computing hardware devices by conducting a series of designed experiments. These include the demonstration of implementing data compression, data filtering, and communication message processing and inter-experiment data computation. The second generation is the Adaptive Processing Template (ADAPT) which is further described in this paper. The next step forward is to make the hardware itself adaptable and the ADAPT pursues this challenge by developing a reconfigurable module that will be capable of functioning efficiently in various applications. ADAPT will take advantage of radiation tolerant RAM-based field programmable gate array (FPGA) technology to develop a reconfigurable processor that combines the flexibility of a general purpose processor running software with the performance of application specific processing hardware for a variety of high performance computing applications.

Darrin, Ann Garrison↗
DOE OSTI2023

More is definitely different: the zebrafish as witness: Comment on ”Structure and function in artificial, zebrafish and human neural networks” by Peng Ji et al.

We report half a century ago, in his foundation paper More is different, Anderson stated the relevance of including multiple scales of interaction, instead of trying to reduce everything to fundamental principles. By doing so, one may witness phenomena that were not expected by looking at each individual components separately. This idea finds indeed numerous realizations such as the synchronization phenomenon, where a multitude of dynamical systems suddenly start to behave coherently together, or the spreading of diseases over entire populations, to name only two examples. But if one would have to give a single example of a system where the multiplicity of interaction scales is of utmost importance, it would probably be the brain. In their review, Ji et al. give a comprehensive overview of the multiple scales of science required in the investigation of structural and functional brain networks, as well as their interplay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A human cell type similar to murine central nervous system perivascular fibroblasts

Highlights: • Human diploid cell lines were established from tumor and non-tumor brain tissue. • Human cells share a common transcriptomic profile with murine perivascular fibroblasts. • Human cells resemble commercially available cell of presumed pericyte origin. The brain vasculature has several specific features, one of them being the blood-brain barrier (BBB), which supports and protects the brain by allowing for the passage of oxygen and nutrients, while at the same time preventing passage of pathogens and toxins. The BBB also prevents efficient delivery of drugs to the brain, e.g. for treatment of brain tumors. In the murine brain, perivascular fibroblasts were recently identified as a novel potential constituent of the BBB. Here we present the existence of human cells that could be the equivalent to the murine brain perivascular fibroblasts. Using RNA sequencing, we show a similar transcriptomic profile of cultured human brain cells and murine perivascular fibroblasts. These data open up a window for new hypotheses on cell types involved in human CNS diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

The NRG3/ERBB4 signaling cascade as a novel therapeutic target for canine glioma

Highlights: • miR-190a was downregulated in canine brain tumors. • miR-190a functioned as a tumor-suppressor through targeting NRG3. • A pan-ERBB inhibitor exhibited a tumor suppressive effect in canine glioma cells. Canine glioma is a common brain tumor with poor prognosis despite surgery and/or radiation therapy. Therefore, newer and more effective treatment modalities are needed. Neuregulin 3 (NRG3) has known to be a ligand of ERBB4. This study aimed to investigate the usefulness of the NRG3/ERBB4 signaling cascade as a novel therapeutic target in canine glioma. We found out that microRNA (miR)-190a was downregulated in canine brain tumor tissues, including glioma and meningioma. miR-190a directly targeted NRG3 and inhibited the growth of canine glioma cells. The level of p-Akt, which is a downstream target of ERBB4 signaling, was decreased by transfection with miR-190a. NRG3 silencing also suppressed cell growth and decreased the levels of p-Akt and p-ERK1/2, and NRG3 overexpression exhibited opposed effects in canine glioma J3T-1 cells. The mRNA level of erbb4 was significantly upregulated in glioma tissues compared with that in normal brain tissues and meningioma tissues. Furthermore, compared with gefitinib and lapatinib, afatinib exerted a greater inhibitory effect on the growth of canine glioma cells. In conclusion, NRG3/ERBB4 signaling is negatively regulated by miR-190a and contributes to the growth of canine glioma cells, indicating that it may be a promising therapeutic target in canine glioma.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Organotypic platform for studying cancer cell metastasis

Highlights: • Liver and brain slices can be stable, realistic in vitro 3D models. • Cell stiffness and adhesion to the tissue play determinant roles in cellular invasion in 3D. • MEK inhibitors can lead to increased cell stiffness and adhesion forces, and consequently reduced cancer cell invasion. • MDA-MB-231 breast cancer cells migrate faster and deeper into brain tissue compared to liver tissue. Metastasis is the leading cause of mortality in cancer patients. To migrate to distant sites, cancer cells would need to adapt their behaviour in response to different tissue environments. Thus, it is essential to study this process in models that can closely replicate the tumour microenvironment. Here, we evaluate the use of organotypic liver and brain slices to study cancer metastasis. Morphological and viability parameters of the slices were monitored daily over 3 days in culture to assess their stability as a realistic 3D tissue platform for in vitro metastatic assays. Using these slices, we evaluated the invasion of MDA-MB-231 breast cancer cells and of a subpopulation that was selected for increased motility. We show that the more aggressive invasion of the selected cells likely resulted not only from their lower stiffness, but also from their lower adhesion to the surrounding tissue. Different invasion patterns in the brain and liver slices were observed for both subpopulations. Cells migrated faster in the brain slices (with an amoeboid-like mode) compared to in the liver slices (where they migrated with mesenchymal or collective migration-like modes). Inhibition of the Ras/MAPK/ERK pathway increased cell stiffness and adhesion forces, which resulted in reduced invasiveness. These results illustrate the potential for organotypic tissue slices to more closely mimic in vivo conditions during cancer cell metastasis than most in vitro models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS’s initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering

Myelinated axons (nerve fibers) efficiently transmit signals throughout the brain via action potentials. Multiple methods that are sensitive to axon orientations, from microscopy to magnetic resonance imaging, aim to reconstruct the brain's structural connectome. As billions of nerve fibers traverse the brain with various possible geometries at each point, resolving fiber crossings is necessary to generate accurate structural connectivity maps. However, doing so with specificity is a challenging task because signals originating from oriented fibers can be influenced by brain (micro)structures unrelated to myelinated axons. X-ray scattering can specifically probe myelinated axons due to the periodicity of the myelin sheath, which yields distinct peaks in the scattering pattern. Here, in this work, we show that small-angle X-ray scattering (SAXS) can be used to detect myelinated, axon-specific fiber crossings. We first demonstrate the capability using strips of human corpus callosum to create artificial double- and triple-crossing fiber geometries, and we then apply the method in mouse, pig, vervet monkey, and human brains. We compare results to polarized light imaging (3D-PLI), tracer experiments, and to outputs from diffusion MRI that sometimes fails to detect crossings. Given its specificity, capability of 3-dimensional sampling and high resolution, SAXS could serve as a ground truth for validating fiber orientations derived using diffusion MRI as well as microscopy-based methods.

59 BASIC BIOLOGICAL SCIENCES↗