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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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5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Classification of orthostatic intolerance through data analytics

Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. Here we use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Electrocardiographic changes predate Parkinson’s disease onset

Autonomic nervous system involvement precedes the motor features of Parkinson’s disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD. Various methods were implemented to extract features from ECGs including simple heart rate variability (HRV) metrics, commonly used signal processing methods, and a Probabilistic Symbolic Pattern Recognition (PSPR) method. Extracted features were analyzed via stepwise logistic regression to distinguish between prodromal cases and controls. Stepwise logistic regression selected four features from PSPR as predictors of PD. The final regression model built on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95% confidence interval of 0.90 [0.80, 0.99]. The five-fold cross-validation process produced an average AUC of 0.835 [0.831, 0.839]. We conclude that cardiac electrical activity provides important information about the likelihood of future PD not captured by classical HRV metrics. Machine learning applied to ECGs may help identify subjects at high risk of having prodromal PD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Non-invasive ventral cervical magnetoneurography as a proxy of in vivo lipopolysaccharide-induced inflammation

Maintenance of autonomic homeostasis is continuously calibrated by sensory fibers of the vagus nerve and sympathetic chain that convey compound action potentials (CAPs) to the central nervous system. Lipopolysaccharide (LPS) intravenous challenge reliably elicits a robust inflammatory response that can resemble systemic inflammation and acute endotoxemia. Here, we administered LPS intravenously in nine healthy subjects while recording ventral cervical magnetoneurography (vcMNG)-derived CAPs at the rostral Right Nodose Ganglion (RNG) and the caudal Right Carotid Artery (RCA) with optically pumped magnetometers (OPM). We observed vcMNG RNG and RCA neural firing rates that tracked changes in TNF-α levels in the systemic circulation. Further, endotype subgroups based on high and low IL-6 responders segregate RNG CAP frequency (at 30-120 min) and based on high and low IL-10 response discriminate RCA CAP frequency (at 0-30 min). These vcMNG tools may enhance understanding and management of the neuroimmune axis that can guide personalized treatment based on an individual’s distinct endophenotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Interactive Materials for Bidirectional Redox-Based Communication

Emerging research indicates that biology routinely uses diffusible redox-active molecules to mediate communication that can span biological systems (e.g., nervous and immune) and even kingdoms (e.g., a microbiome and its plant/animal host). This redox modality also provides new opportunities to create interactive materials that can communicate with living systems. Here in this paper, it is reported that the fabrication of a redox-active hydrogel film can autonomously synthesize a H 2 O 2 signaling molecule for communication with a bacterial population. Specifically, a catechol-conjugated/crosslinked 4-armed thiolated poly(ethylene glycol) hydrogel film is electrochemically fabricated in which the added catechol moieties confer redox activity: the film can accept electrons from biological reductants (e.g., ascorbate) and donate electrons to O 2 to generate H 2 O 2 . Electron-transfer from an Escherichia coli culture poises this film to generate the H 2 O 2 signaling molecule that can induce bacterial gene expression from a redox-responsive operon. Overall, this work demonstrates that catecholic materials can participate in redox-based interactions that elicit specific biological responses, and also suggests the possibility that natural phenolics may be a ubiquitous biological example of interactive materials.

electrobiofabrication↗
DOE OSTI2020

Organismic materials for beyond von Neumann machines

The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as "organismic materials") therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term "correlated" refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator-metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable with different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO 2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. Finally, we conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.

42 ENGINEERING↗