Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hypothesis learning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

High-school Student Teams in a National NASA Microgravity Science Competition

The Dropping In a Microgravity Environment or DIME competition for high-school-aged student teams has completed the first year for nationwide eligibility after two regional pilot years. With the expanded geographic participation and increased complexity of experiments, new lessons were learned by the DIME staff. A team participating in DIME will research the field of microgravity, develop a hypothesis, and prepare a proposal for an experiment to be conducted in a NASA microgravity drop tower. A team of NASA scientists and engineers will select the top proposals and then the selected teams will design and build their experiment apparatus. When completed, team representatives will visit NASA Glenn in Cleveland, Ohio to operate their experiment in the 2.2 Second Drop Tower and participate in workshops and center tours. NASA participates in a wide variety of educational activities including competitive events. There are competitive events sponsored by NASA (e.g. NASA Student Involvement Program) and student teams mentored by NASA centers (e.g. For Inspiration and Recognition of Science and Technology Robotics Competition). This participation by NASA in these public forums serves to bring the excitement of aerospace science to students and educators.Researchers from academic institutions, NASA, and industry utilize the 2.2 Second Drop Tower at NASA Glenn Research Center in Cleveland, Ohio for microgravity research. The researcher may be able to complete the suite of experiments in the drop tower but many experiments are precursor experiments for spaceflight experiments. The short turnaround time for an experiment's operations (45 minutes) and ready access to experiment carriers makes the facility amenable for use in a student program. The pilot year for DIME was conducted during the 2000-2001 school year with invitations sent out to Ohio- based schools and organizations. A second pilot year was conducted during the 2001-2002 school year for teams in the six-state region of Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. The third year for DIME was conducted during the 2002-2003 school year for teams from the fifty United States, the District of Columbia, and Puerto Rico. An annual national DIME program is planned for the foreseeable future. Presented in this paper will be a description of DIME, an overview of the planning and execution of such a program, results from the first three years, and lessons learned from the first national competition.

DeLombard, Richard↗
NASA NTRS1992

The Extrapolation of Elementary Sequences

We study sequence extrapolation as a stream-learning problem. Input examples are a stream of data elements of the same type (integers, strings, etc.), and the problem is to construct a hypothesis that both explains the observed sequence of examples and extrapolates the rest of the stream. A primary objective -- and one that distinguishes this work from previous extrapolation algorithms -- is that the same algorithm be able to extrapolate sequences over a variety of different types, including integers, strings, and trees. We define a generous family of constructive data types, and define as our learning bias a stream language called elementary stream descriptions. We then give an algorithm that extrapolates elementary descriptions over constructive datatypes and prove that it learns correctly. For freely-generated types, we prove a polynomial time bound on descriptions of bounded complexity. An especially interesting feature of this work is the ability to provide quantitative measures of confidence in competing hypotheses, using a Bayesian model of prediction.

Laird, Philip↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

Significance Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Mars Geophysics

The titles in this section include: 1) An Extraordinary Magnetic Field Map of Mars; 2) Mapping Weak Crustal Magnetic Fields on Mars with Electron Reflectometry; 3) Analytic Signal in the Interpretation of Mars Southern Highlands Magnetic Field; 4) Modeling of Major Martian Magnetic Anomalies: Further Evidence for Polar Reorientations During the Noachian; 5) An Improved Model of the Crustal Structure of Mars; 6) Geologic Evolution of the Martian Dichotomy and Plains Magnetization in the Ismenius Area of Mars; 7) Relaxation of the Martian Crustal Dichotomy Boundary in the Ismenius Region; 8) Localized Tharsis Loading on Mars: Testing the Membrane Surface Hypothesis; 9) Thermal Stresses and Tharsis Loading: Implications for Wrinkle Ridge Formation on Mars; 10) What Can be Learned about the Martian Lithosphere from Gravity and Topography Data? 11) A Gravity Analysis of the Subsurface Structure of the Utopia Impact Basin; 12) Mechanics of Utopia Basin on Mars; 13) Burying the 'Buried Channels' on Mars: An Alternative Explanation.

Source record↗
DOE OSTI2024

Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing

The Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing project objective is to automatically tune machining parameter predictions from physics-based models using process data and Bayesian machine learning. The intent is to enable a step change in aerospace manufacturing by combining machine learning, physics-based process models, and sensors/data in a comprehensive digital environment that simultaneously considers the computer numerically controlled (CNC) machining center capabilities, the workpiece material and geometry, and the workpiece support (fixturing). The project hypothesis is that this combination will enable improved performance in machining operations.

42 ENGINEERING↗
DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

GeneLab for High Schools: Data Mining for the Next Generation

Modern biological sciences have become increasingly based on molecular biology and high-throughput molecular techniques, such as genomics, transcriptomics, and proteomics. NASA Scientists and the NASA Space Biology Program have aimed to examine the fundamental building blocks of life (RNA, DNA and protein) in order to understand the response of living organisms to space and aid in fundamental research discoveries on Earth. In an effort to enable NASA funded science to be available to everyone, NASA has collected the data from omics studies and curated them in a data system called GeneLab. Whilst most college-level interns, academics and other scientists have had some interaction with omics data sets and analysis tools, high school students often have not. Therefore, the Space Biology Program is implementing a new Summer Program for high-school students that aims to inspire the next generation of scientists to learn about and get involved in space research using GeneLabs Data System. The program consists of three main components core learning modules, focused on developing students knowledge on the Space Biology Program and Space Biology research, Genelab and the data system, and previous research conducted on model organisms in space; networking and team work, enabling students to interact with guest lecturers from local universities and their fellow peers, and also enabling them to visit local universities and genomics centers around the Bay area; and finally an independent learning project, whereby students will be required to form small groups, analyze a dataset on the Genelab platform, generate a hypothesis and develop a research plan to test their hypothesis. This program will not only help inspire high-school students to become involved in space-based research but will also help them develop key critical thinking and bioinformatics skills required for most college degrees and furthermore, will enable them to establish networks with their peers and connections with university Professors that may help them achieve their educational goals.

genelab↗
NASA NTRS2018

Using Board Games as Subject Matter for Developing Expertise in Model-Based Systems Engineering

As more organizations transition from traditional document-centric systems engineering to a model-based approach, many are challenged to train their staff in new languages, tools, and methodologies, while managing the expectations of stakeholders and their expected model outcomes. In particular, challenges associated with learning a new modeling language and developing skills in the 'art' of modeling present organizations with formidable obstacles to realizing this transition. This paper hypothesizes that systems engineers may more readily learn how to correctly model with SysML, and develop intuition about the art of modeling and using patterns, if their learning references a commonly and thoroughly-understood subject, such as a board game. This paper presents a case for the use of board games as subject matter for new modelers. It demonstrates the concept with a sample model of Hasbro's popular board game, Monopoly, and discusses the limitations of this approach and potential adaptations that may broaden the applicability of the learned skills to projects. Finally, results from a small feasibility assessment and concepts for more formal study to evaluate the hypothesis are presented.

Model-Based Systems Engineering↗
DOE OSTI2025

Causal machine learning uncovers conditions for convective intensification driven by organic and sulfate aerosols

Aerosols are often hypothesized to invigorate deep convective clouds (DCCs), but observational evidence remains limited and inconclusive. Clarifying this hypothesis is critical for regions vulnerable to thunderstorms and flooding, particularly highly polluted coastal cities. Leveraging a novel causal discovery–inference pipeline and high-resolution observations near Houston, TX, we identify multiple causal pathways among aerosols (mostly organic and sulfate), DCCs, and meteorological factors. However, a direct causal link from aerosols to DCCs is found to be uncommon, occurring in less than 35% of analyzed scenarios, and is characterized by strong conditionality and nonlinearity. When aerosol impacts on DCCs do occur, they can be substantial, enhancing DCC core heights by approximately 1.7 km, with 92% of this effect concentrated in warmer-phase cloud regions. Notably, the presence of sea breezes and the inclusion of all measured aerosol particles each enhance DCCs in over 95% of aerosol-sensitive cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Observation of high-energy neutrinos from the Galactic plane

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth’s atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

Science & Technology - Other Topics↗
DOE OSTI2021

Assimilation of Multiscale Data into Multifidelity Biogeochemical Models (Final Report)

Quantitative predictions of subsurface processes rely on computational models that capture, with different degrees of fidelity, complex interactions between hydrologic and biogeochemical processes. Molecular- and pore-scale models provide a high-fidelity representation of these processes but are impractical at the field scale. Reduced complexity (e.g., field-scale or data-driven) models sacrifice some degree of fidelity in favor of computational efficiency. Uncertainty and assimilation of data into model predictions, pose a question of model selection: Given a significant difference in computational cost, can a lower-fidelity model be preferable to its higher-fidelity counterpart? Since a predictive model must be computable in reasonable time it has to operate at the field scale, with molecular- and pore-scale data (obtained either from simulations or measurements) determining both the model's structure and parameters. Availability of such multi-resolution data raises a question hitherto undressed in hydrogeology: Can a coarse model dynamically ``learn'' its own structure (e.g., adjust the reaction pathways in its transport module) as more fine-scale data become available during simulations? Our results in multifidelity simulations, data assimilation, and machine learning led us to hypothesize that the answer to these questions is ``yes''. The overarching goal of this project was to confirm this hypothesis by developing scalable, computationally efficient tools for assimilation of data into multiscale models, in which fine-scale data and simulations dynamically inform and autonomously modify coarse-scale models. Our research led to nine manuscripts, three of which have been published and the other six are currently under review.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Synthesis of motif and symmetry for accelerated learning, discovery, and design of electronic structures for energy conversion applications (Final Technical Report)

The overall goal of the projects is to develop a framework to incorporate structure motifs and crystal/orbital symmetries into the data-driven materials discovery infrastructure. The PI proposed to develop structure-motif- and symmetry-based graph convolutional networks for effective learning and efficient predictions of electronic structures and related properties. Fundamental understanding of the roles of structure motif and symmetry will establish new hypothesis and design rules, which will be combined with high-throughput computations based on density functional theory to discover novel light absorbers, transparent conductors, as well as 2D light emitting materials and heterojunctions for optoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

NREM sleep as a novel protective cognitive reserve factor in the face of Alzheimer's disease pathology

Alzheimer’s disease (AD) pathology impairs cognitive function. Yet some individuals with high amounts of AD pathology suffer marked memory impairment, while others with the same degree of pathology burden show little impairment. Why is this? One proposed explanation is cognitive reserve i.e., factors that confer resilience against, or compensation for the effects of AD pathology. Deep NREM slow wave sleep (SWS) is recognized to enhance functions of learning and memory in healthy older adults. However, that the quality of NREM SWS (NREM slow wave activity, SWA) represents a novel cognitive reserve factor in older adults with AD pathology, thereby providing compensation against memory dysfunction otherwise caused by high AD pathology burden, remains unknown. Here, we tested this hypothesis in cognitively normal older adults (N = 62) by combining 11 C-PiB (Pittsburgh compound B) positron emission tomography (PET) scanning for the quantification of β-amyloid (Aβ) with sleep electroencephalography (EEG) recordings to quantify NREM SWA and a hippocampal-dependent face-name learning task. We demonstrated that NREM SWA significantly moderates the effect of Aβ status on memory function. Specifically, NREM SWA selectively supported superior memory function in individuals suffering high Aβ burden, i.e., those most in need of cognitive reserve (B = 2.694, p = 0.019). In contrast, those without significant Aβ pathological burden, and thus without the same need for cognitive reserve, did not similarly benefit from the presence of NREM SWA (B = -0.115, p = 0.876). This interaction between NREM SWA and Aβ status predicting memory function was significant after correcting for age, sex, Body Mass Index, gray matter atrophy, and previously identified cognitive reserve factors, such as education and physical activity (p = 0.042). These findings indicate that NREM SWA is a novel cognitive reserve factor providing resilience against the memory impairment otherwise caused by high AD pathology burden. Furthermore, this cognitive reserve function of NREM SWA remained significant when accounting both for covariates, and factors previously linked to resilience, suggesting that sleep might be an independent cognitive reserve resource. Beyond such mechanistic insights are potential therapeutic implications. Unlike many other cognitive reserve factors (e.g., years of education, prior job complexity), sleep is a modifiable factor. As such, it represents an intervention possibility that may aid the preservation of cognitive function in the face of AD pathology, both present moment and longitudinally.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Downhole Sensing and Event-Driven Sensor Fusion for Depth-of-Cut Based Autonomous Fault Response and Drilling Optimization

Achieving robust and efficient drilling is a critical part of reducing the cost of geothermal energy exploration and extraction. Drilling performance is often evaluated using one or more of three key metrics: depth of cut (DOC), rate of penetration (ROP), and mechanical specific energy (MSE). All three of these quantities are related to each other. DOC refers to the depth a bit penetrates into rock during drilling. This is an important quantity for estimating bit behavior. ROP is the simply the DOC multiplied by the rotational rate, and represents how quickly the drill bit is advancing through the ground. ROP is often the parameter used for drilling control and optimization. Finally, MSE provides insight into drilling efficiency and rock type. MSE calculations rely on ROP, drilling force, and drilling torque. Surface-based sensors at the top of the drill are often used to measure all these quantities. However, top-hole measurements can deviate substantially from the behavior at the bit due to lag, vibrations, and friction. Therefore, relying only on top-hole information can lead to suboptimal drilling control. In this work, we describe recent progress towards estimating ROP, DOC, and MSE using down-hole sensing. We assume down-hole measurements of torque, weight-on-bit (WOB). Our hypothesis is that these measurements can provide more rapid and accurate measures of drilling performance. We show how a multi-layer perceptron (MLP) machine learning algorithm can provide rapid and accurate performance when evaluated on experimental data taken from Sandia’s Hard Rock Drilling Facility. In addition, we implement our algorithms on an embedded system intended to emulate a bottom-hole-assembly for sensing and estimation. Our experimental results show that DOC can be estimated accurately and in real-time. These estimates when combined with measurements for rotary speed, torque, and force can provide improved estimates for ROP and MSE. These results have the potential to enable better drilling assessment, improved control, and extended component lifetimes.

15 GEOTHERMAL ENERGY↗
NASA NTRS2003

The Effect of Spaceflight on the Ultrastructure of the Cerebellum

In weightlessness, astronauts and cosmonauts may experience postural illusions as well as motion sickness symptoms known as the space adaptation syndrome. Upon return to Earth, they have irregularities in posture and balance. The adaptation to microgravity and subsequent re-adaptation to Earth occurs over several days. At the cellular level, a process called neuronal plasticity may mediate this adaptation. The term plasticity refers to the flexibility and modifiability in the architecture and functions of the nervous system. In fact, plastic changes are thought to underlie not just behavioral adaptation, but also the more generalized phenomena of learning and memory. The goal of this experiment was to identify some of the structural alterations that occur in the rat brain during the sensory and motor adaptation to microgravity. One brain region where plasticity has been studied extensively is the cerebellar cortex-a structure thought to be critical for motor control, coordination, the timing of movements, and, most relevant to the present experiment, motor learning. Also, there are direct as well as indirect connections between projections from the gravity-sensing otolith organs and several subregions of the cerebellum. We tested the hypothesis that alterations in the ultrastructural (the structure within the cell) architecture of rat cerebellar cortex occur during the early period of adaptation to microgravity, as the cerebellum adapts to the absence of the usual gravitational inputs. The results show ultrastructural evidence for neuronal plasticity in the central nervous system of adult rats after 24 hours of spaceflight. Qualitative studies conducted on tissue from the cerebellar cortex (specifically, the nodulus of the cerebellum) indicate that ultrastructural signs of plasticity are present in the cerebellar zones that receive input from the gravity-sensing organs in the inner ear (the otoliths). These changes are not observed in this region in cagematched ground control animals. The specific changes include the formation of lamellar bodies, profoundly enlarged Purkinje cell mitochondria, the presence of inter-neuronal cellular protrusions in the molecular layer, and signs of degeneration in the distal dendrites of the Purkinje cells. Since these morphologic signs are not apparent in the control animals, they are not likely to be due to caging or tissue processing effects. The particular nature of the structural alterations in the nodulus, most notably the formation of lamellar bodies and the presence of degeneration, further suggests that excitotoxicity (damaging overstimulation of neurons) may play a role in the short-term neural response to spaceflight. These findings suggest a structural basis for the neuronal and synaptic plasticity accompanying the central nervous system response to altered gravity and help identify the cellular bases underlying the vestibular abnormalities experienced by astronauts during periods of adaptation and re-adaptation to different gravitational forces. Also, since the short- and long-term changes in neural structure occurring during such periods of adaptation resemble the neuronal alterations that occur in some neurologic disorders such as stroke, these findings may offer guidance in the development of strategies for rehabilitation and treatment of such disorders.

Holstein, Gay R.↗