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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis

Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain, spinal cord and optic nerve leading to demyelination. Focal demyelination is associated with relapsing-remitting multiple sclerosis, while progressive forms of the disease show axonal degeneration and neuronal loss. The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity. MicroRNAs (miRNAs) are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases. A review of recent studies with the experimental autoimmune encephalomyelitis animal model (mostly female mice 6–12 weeks of age) has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset (asymptomatic) stage when assessed in blood plasma and urine exosomes, and spinal cord tissue. The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes, brain and spinal cord tissue, and at the post-peak (chronic) stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue. Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease. Interestingly, experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a, miR-23b, miR-497, miR-26a, and miR-20b, or by suppression of miR-182, miR-181c, miR-223, miR-155, and miR873. Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course. Additionally, studies should be performed with male mice of a similar age, and with aged male and female mice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Controlled Formation of Conduction Channels in Memristive Devices Observed by X–ray Multimodal Imaging

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. Furthermore, this work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Locus coeruleus catecholamines link neuroticism and vulnerability to tau pathology in aging

Higher neuroticism is a risk factor for Alzheimer's disease (AD), and is implicated in disordered stress responses. The locus coeruleus (LC)-catecholamine system is activated during perceived threat and is a centerpiece of developing models of the pathophysiology of AD, as it is the first brain region to develop abnormal tau. We examined relationships among the "Big 5" personality traits, LC catecholamine synthesis capacity measured with [ 18 F]Fluoro-m-tyrosine PET, and tau burden measured with [ 18 F]Flortaucipir PET in cognitively normal older adults (n = 47). β-amyloid (Aβ) status was determined using [ 11 C]Pittsburgh compound B PET (n = 14 Aβ positive). Lower LC catecholamine synthesis capacity was associated with higher neuroticism, more depressive symptoms as measured by the Geriatric Depression Scale, and higher amygdala tau-PET binding. Exploratory analyses with other personality traits revealed that low trait conscientiousness was also related to both lower LC catecholamine synthesis capacity, and more depressive symptoms. A significant indirect path linked both high neuroticism and low conscientiousness to greater amygdala tau burden via their mutual association with low LC catecholamine synthesis capacity. Together, these findings reveal LC catecholamine synthesis capacity to be a promising marker of affective health and pathology burden in aging, and identifies candidate neurobiological mechanisms for the effect of personality on increased vulnerability to dementia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Symmetrically tuned large-volume conic shell-cavities for axion searches

In an earlier paper [1], a new class of thin-shell cavities were proposed to evade the steep frequency scaling of conventional axion haloscopes. In this follow-up work, we see that a generalized conic geometry enables robust frequency-tuning for these large-volume cm-wave cavities. The frequency-defining dimension of a conic shell-cavity changes symmetrically and uniformly during tuning, maintaining a high axion coupling efficiency (the "form factor") to an external solenoid field. It is further shown that such tunable geometry is not restricted to circular cones. A general prescription for arbitrary volume-filling conic shell-cavities is developed and direct solutions are obtained for the created numerical models. Additionally, the largest of the realized designs is a meandering "brain" cavity that is tunable over a frequency range of 20%. The scan rate of this cavity is three orders of magnitude larger than that of a scaled cylindrical cavity used in the current generation experiments. The prospects for such a large improvement in the scan rate should motivate R& D efforts in fabrication and other implementation techniques. If these engineering challenges can be met, cavity-based axion haloscopes can stay competitive at frequencies higher than a few GHz. We propose an experimental configuration at 20 GHz (~80 μeV) using an array of brain cavities and compare it with other proposals for similar frequencies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Eucalyptus Wood Smoke Extract Elicits a Dose-Dependent Effect in Brain Endothelial Cells

The frequency, duration, and size of wildfires have been increasing, and the inhalation of wildfire smoke particles poses a significant risk to human health. Epidemiological studies have shown that wildfire smoke exposure is positively associated with cognitive and neurological dysfunctions. However, there is a significant gap in knowledge on how wildfire smoke exposure can affect the blood–brain barrier and cause molecular and cellular changes in the brain. Our study aims to determine the acute effect of smoldering eucalyptus wood smoke extract (WSE) on brain endothelial cells for potential neurotoxicity in vitro. Primary human brain microvascular endothelial cells (HBMEC) and immortalized human brain endothelial cell line (hCMEC/D3) were treated with different doses of WSE for 24 h. WSE treatment resulted in a dose-dependent increase in IL-8 in both HBMEC and hCMEC/D3. RNA-seq analyses showed a dose-dependent upregulation of genes involved in aryl hydrocarbon receptor (AhR) and nuclear factor erythroid 2-related factor 2 (NRF2) pathways and a decrease in tight junction markers in both HBMEC and hCMEC/D3. When comparing untreated controls, RNA-seq analyses showed that HBMEC have a higher expression of tight junction markers compared to hCMEC/D3. In summary, our study found that 24 h WSE treatment increases IL-8 production dose-dependently and decreases tight junction markers in both HBMEC and hCMEC/D3 that may be mediated through the AhR and NRF2 pathways, and HBMEC could be a better in vitro model for studying the effect of wood smoke extract or particles on brain endothelial cells.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

Model for the computation of self-motion in biological systems

A technique is presented by which direction- and speed-tuned cells, such as those commonly found in the middle temporal region of the primate brain, can be utilized to analyze the patterns of retinal image motion that are generated during observer movement through the environment. The developed model determines heading by finding the peak response in a population of detectors or neurons each tuned to a particular heading direction. It is suggested that a complex interaction of multiple cell networks is required for the solution of the self-motion problem in the primate brain.

Perrone, John A.↗
NASA NTRS1993

Development of a model to assess orthostatic responses

A major change for crewmembers during weightlessness in microgravity is the redistribution of body fluids from the legs into the abdomen, thorax, and head. The fluids continue to be sequestered in these areas throughout the flight. Upon reentry into gravity on landing, these same body fluids are displaced again to their normal locations, however, not without hazardous incidence to the crewmembers. The problem remains that upon landing, crewmembers are subject to orthostasis, that is, the blood flowing into the legs reduces the blood supply to the brain and may result in the crewmember fainting. The purpose of this study was to develop a model of testing orthostatic responses of blood pressure regulating mechanisms of the cardiovascular system, when challenged, to maintain blood pressure to the brain. To accomplish this, subjects' responses were assessed as they proceeded from the supine position of progressive head-up tilt positions of 30 deg, 60 deg, and 90 deg angles. A convenience sample consisted of 21 subjects, females (N=11) and males (N=10), selected from a list of potential subjects available through the NASA subject screening office. The methodology included all non-invasive measurements of blood pressure, heart rate, echocardiograms, cardiac output, cardiac stroke volume, fluid shifts in the thorax, ventricular ejection and velocity times, and skin blood perfusion. The Fischer statistical analysis was done of all data with the significance level at .05. Significant differences were demonstrated in many instances of changes of posture for all variables. Based on the significance of the findings of this study, this model for assessing orthostatic responses does provide an adequate challenge to the blood pressure regulatory systems. While individuals may use different adaptations to incremental changes in gravity, the subjects, in aggregate, demonstrated significant adaptive cardiovascular changes to orthostatic challenges which were presented to them.

Rubin, Marilyn↗
NASA NTRS1989

A design philosophy for multi-layer neural networks with applications to robot control

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Vadiee, Nader↗
DOE OSTI2021

Development of a Deep Learning Model for Predicting the Drag Coefficients of Spherical and Non-Spherical Particles,

There is yet to be a well-established drag model for non-spherical particles required in a particle-laden flow that could cover a wide range of sphericities. This talk will explore the development of a general drag model for non-spherical particles by applying deep learning using available experimental data available in the literature. The integration of several raw experimental measurements from different sources and research directions allows the training of robust Artificial Intelligence and Machine Learning (ML) models. Neural networks are an ML approach inspired by the inner biological workings of the brain. This work aims to develop a Deep Neural Network (DNN) that predicts drag coefficient values with the ability to adapt appropriately to unseen data. Given the limited number of data points available and the variance found within the data collected from various sources, challenges may arise when looking to train the model. Our study tests and implements various model regularization techniques and assesses different loss and activation functions for the proposed DNN. The proposed model considers a broader range of features other than sphericity and Reynold number. These features include density ratio, solid volume fraction, lengthwise and crosswise sphericity, and more. Furthermore, we present the features that play a significant role in predicting different drag coefficients through feature importance. Within the investigated parameter ranges in this study, the following conclusions can be achieved and summarized below: • An improved drag coefficient model can be developed by considering more features such as, aspect ratio, lengthwise sphericity, crosswise sphericity, and density ratio. • DNN model can predict better results compared to traditional methods using MAE metric. • The proposed model addresses data challenges such as limited data and extreme data points through expanded feature-set and regularization. • Three major features that mostly affect the drag coefficient were identified from a feature importance analysis.

Presa-Reyes, Maria↗
DOE OSTI2023

New cyclophilin D inhibitor rescues mitochondrial and cognitive function in Alzheimer’s disease

Abstract Mitochondrial dysfunction is an early pathological feature of Alzheimer disease and plays a crucial role in the development and progression of Alzheimer’s disease. Strategies to rescue mitochondrial function and cognition remain to be explored. Cyclophilin D (CypD), the peptidylprolyl isomerase F (PPIase), is a key component in opening the mitochondrial membrane permeability transition pore, leading to mitochondrial dysfunction and cell death. Blocking membrane permeability transition pore opening by inhibiting CypD activity is a promising therapeutic approach for Alzheimer’s disease. However, there is currently no effective CypD inhibitor for Alzheimer’s disease, with previous candidates demonstrating high toxicity, poor ability to cross the blood–brain barrier, compromised biocompatibility and low selectivity. Here, we report a new class of non-toxic and biocompatible CypD inhibitor, ebselen, using a conventional PPIase assay to screen a library of ∼2000 FDA-approved drugs with crystallographic analysis of the CypD-ebselen crystal structure (PDB code: 8EJX). More importantly, we assessed the effects of genetic and pharmacological blockade of CypD on Alzheimer’s disease mitochondrial and glycolytic bioenergetics in Alzheimer’s disease-derived mitochondrial cybrid cells, an ex vivo human sporadic Alzheimer’s disease mitochondrial model, and on synaptic function, inflammatory response and learning and memory in Alzheimer’s disease mouse models. Inhibition of CypD by ebselen protects against sporadic Alzheimer’s disease- and amyloid-β-induced mitochondrial and glycolytic perturbation, synaptic and cognitive dysfunction, together with suppressing neuroinflammation in the brain of Alzheimer’s disease mouse models, which is linked to CypD-related membrane permeability transition pore formation. Thus, CypD inhibitors have the potential to slow the progression of neurodegenerative diseases, including Alzheimer’s disease, by boosting mitochondrial bioenergetics and improving synaptic and cognitive function.

Neurosciences & Neurology↗
DOE OSTI2021

Open Building Control

Best practice control sequences are often not implemented correctly, or are not implemented at all, in large commercial buildings. This typically leads to 10-30 percent energy waste, along with reduced occupant productivity and unnecessary equipment wear. The current process of designing and implementing such control sequences is a manual process that starts with designers who often don’t have adequate training, then requires controls programmers to interpret and program a verbose written sequence. This process has been shown to fail to deliver high performance control sequences at scale. The Open Building Control project digitizes the current control delivery process. The project is developing tools for system designers to select control sequences, assess their energy performance and load flexibility potential using whole building simulation, specify the sequence for implementation using machine-to-machine translation by a control provider and formally testing the as-installed sequences by a commissioning agent. The project developed tools for each stage of this delivery process. The key innovation of the project is the development of the Control Description Language, a language that allows such a digitized control delivery process with end-to-end verification. Libraries of control sequences have been implemented using the Control Description Language, and their performance has been demonstrated using whole building energy simulation. An automated translation of such sequences to a commercial control product line has been conducted using a prototype translator. Tools for formal verification of as-installed control sequences relative to their specification have been developed and demonstrated. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) started the process of forming a committee to make this language an ASHRAE/ANSI Standard. This new standard will complement existing and emerging ASHRAE standards for building communication and semantic modeling by providing a standard for expressing the control logic - the actual brain of the building. We expect this language and the process it enables to be an important contribution to the deployment of high performance building control sequences at scale because it allows taming the complexity of the control delivery process, which is continually increasing due to the need for higher performance and increased load flexibility to meet goals for net zero energy and increased renewable integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Twentieth Annual Conference on Manual Control, Volume 2

Volume II contains thirty two complete manuscripts and five abstracts. The topics covered include the application of event-related brain potential analysis to operational problems, the subjective evaluation of workload, mental models, training, crew interaction analysis, multiple task performance, and the measurement of workload and performance in simulation.

Hart, S. G.↗
DOE OSTI2022

Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater

Introduction Bodies recovered from water, especially in the late phase of decomposition, pose difficulties to the investigating authorities. Various methods have been proposed for postmortem submersion interval (PMSI) estimation and drowning identification, but some limitations remain. Many recent studies have proved the value of microbiota succession in viscera for postmortem interval estimation. Nevertheless, the visceral microbiota succession and its application for PMSI estimation and drowning identification require further investigation. Methods In the current study, mouse drowning and CO 2 asphyxia models were developed, and cadavers were immersed in freshwater for 0 to 14 days. Microbial communities in the liver and brain were characterized via 16S rDNA high-throughput sequencing. Results Only livers and brains collected from 5 to 14 days postmortem were qualified for sequencing. There was significant variation between microbiota from liver and brain. Differences in microbiota between the cadavers of mice that had drowned and those only subjected to postmortem submersion decreased over the PMSI. Significant successions in microbial communities were observed among the different subgroups within the late phase of the PMSI in livers and brains. Eighteen taxa in the liver which were mainly related to Clostridium_sensu_stricto and Aeromonas , and 26 taxa in the brain which were mainly belonged to Clostridium_sensu_stricto , Acetobacteroides , and Limnochorda , were selected as potential biomarkers for PMSI estimation based on a random forest algorithm. The PMSI estimation models established yielded accurate prediction results with mean absolute errors ± the standard error of 1.282 ± 0.189 d for the liver and 0.989 ± 0.237 d for the brain. Conclusions The present study provides novel information on visceral postmortem microbiota succession in corpses submerged in freshwater which sheds new light on PMSI estimation based on the liver and brain in forensic practice.

Wang, Linlin↗
DOE OSTI2024

Modeling the Impacts of Material Properties on Oscillatory Neuron Behavior

In this study, neuromorphic computing, which mimics the functions of biological brains, offers improvements in both latency and energy efficiency over typical von Neumann computing architectures. Spiking neural networks can be especially power-efficient because they encode information temporally and can use more sparse electrical inputs. Here, we study the design of volatile memristors (variable resistors with memory) for neuronal devices, with particular consideration toward the feasibility of all-on-chip oscillation using built-in capacitance. We use circuit simulations to model the behavior of oscillator neurons with a range of realistic material properties. We find that energy inputs increase with insulating-phase resistivity, thermal conductivity, and device aspect ratio. However, we also find that the minimum capacitance needed for oscillation decreases with increasing insulating-phase resistivity, which opposes the constraints for power efficiency. Based on published data on NbO 2 , VO 2 , and EuNiO 3 , we find that existing materials can be engineered for all-on-chip spiking using their parasitic capacitance.

36 MATERIALS SCIENCE↗
NASA NTRS1998

Toward a More Robust Pruning Procedure for MLP Networks

Choosing a proper neural network architecture is a problem of great practical importance. Smaller models mean not only simpler designs but also lower variance for parameter estimation and network prediction. The widespread utilization of neural networks in modeling highlights an issue in human factors. The procedure of building neural models should find an appropriate level of model complexity in a more or less automatic fashion to make it less prone to human subjectivity. In this paper we present a Singular Value Decomposition based node elimination technique and enhanced implementation of the Optimal Brain Surgeon algorithm. Combining both methods creates a powerful pruning engine that can be used for tuning feedforward connectionist models. The performance of the proposed method is demonstrated by adjusting the structure of a multi-input multi-output model used to calibrate a six-component wind tunnel strain gage.

Stepniewski, Slawomir W.↗
DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗