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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Illuminating gut microbiome signaling pathways that regulate host circadian rhythms

The gut microbiome affects host physiology by modulating nutrient acquisition and creating chemical signals that are received by host receptors. The molecular details of the orchestration of these microbial signaling processes remain largely unclear. We used a function-centric approach to interrogate the mechanistic links between gut microbiome metabolism and host circadian rhythms. We used rodent models, including germ-free mice, to determine how the gut microbiome influences host chronobiology by assaying metabolites in the gut and in circulation, and we used activity-based protein profiling to provide direct functional links to individual taxa. We specifically investigated how externally imposed behavioral schedules, such as feeding schedules, can desynchronize biological rhythms in metabolic organs (e.g., the gut, pancreas, and liver) from the central circadian rhythms regulated by the brain. We hypothesize that the gut microbiome creates chemical signals that can be entrained by feeding/fasting schedule to induce internal metabolic misalignment. Elaborating on the findings of this project will elucidate the chemical signals from the microbiome that modulate biological rhythms in the host’s metabolic organs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

2023 SAGE-Camp Report

The 7 th Summer Physics Camp for Young Women was successfully held in person in 2023 from June 5 th to 16 th at the New Mexico School for the Arts in Santa Fe, NM at Hilo Intermediate School in Hawaii. This year’s camp was dedicated to the topic of Energy Security and was made possible thanks to the strong collaboration of Los Alamos, Sandia and Hawaii teams and the logistical and financial support of Los Alamos and Sandia National Laboratories, New Mexico Consortium, SAGE- Moore Foundation, LANL Foundation, ACS, IEE, APS four corners, N3B, Hawaii Museum of Science and Technology, New Mexico School for the Arts (NMSA) and Tech Source. The camp mobilized more than 120 volunteers who made the camp a success. The camp is free of charge to the students and included free lunch and snacks for the busy brains to have plenty of energy, also included all materials needed for the hands-on activities (like drone building, crystal structure, solar panels, wind turbine fabrication, soldering, coding etc) and also a stipend for students who attended for the full two weeks and for two educators and two student mentors in NM. The camp offered 32 high school students from New Mexico and 8 from Hawaii a unique opportunity to explore science topics and meet a broad range of role model professionals across STEM fields including astrophysics, cybersecurity, Energy fields, space science, engineering, biophysics, environmental science, robotics, computer science, nuclear engineering, radiological science, physics and chemistry. With nearly 120 volunteers who came mainly from Los Alamos National Laboratory (66%) and Sandia national laboratories (18%), two funded educators from NM, Dr. Weldon Beauchamp and Dr. Ellee Cook, and two educators from Hilo, Dr. Pascale Creek Pinner and LeAnn Ragasa, the camp was educationally sound and extremely varied. The collaboration with school educators is critical for the goal of this camp to not only impact students' lives but also improve STEM education in NM and Hawaii. The ultimate goal of the camp is to increase higher education aspirations of students, empower them to consider careers in STEM and learn more about the opportunities available to them in our local colleges and DOE National Laboratories. In addition, the camp also hired 2 past students as student mentors, Megan Odom and Elisea Jackson, who currently attend NMSA and were students at the camp in 2022 when it was virtual. The in-person camp which aims at empowering under-represented minorities in STEM in our community received more than 52 applications this year from all over NM and 8 applications from Hawaii. Our selection criteria are based on diversity, equity and inclusion, and students for whom the camp can be a life-changing opportunity are given a chance to attend the camp. During COVID, the camp was held virtually and gave the opportunity to students from remote areas in NM and Hawaii to attend from their homes. This year, fantastic families supported students everyday even when home was in remote areas in NM like Lea county, Sandoval county, Bernalillo or Mora county. The organizers hope next year they can offer a residential option for students from remote areas.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery

Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

IER 499 Thermal Epithermal eXperiments with Highly Enriched Uranium and Chlorine (TEX-Cl) CED-3B

The Thermal Epithermal eXperiments (TEX) with highly enriched uranium (HEU) and chlorine (TEX Cl) experiment, IER 499, was executed at the National Criticality Experiments Research Center (NCERC) in July - August 2024 by the following members of the Advanced Nuclear Technology Group at Los Alamos National Laboratory: Theresa Cutler, Peter Brain, Travis Grove, Rene Sanchez, Alex McSpaden, Kristin Stolte, Zach Lemke, Kenny Valdez, Charlie Kiehne. The experiment was born out of necessity for nuclear criticality safety validation of the capture cross section of 35 Cl. This cross section plays an important role in nuclear criticality safety at both Los Alamos National Laboratory (LANL) and the Y-12 National Security Complex. This work sought to validate the Cl capture cross section through the construction and measurement of three separate critical configurations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
DOE OSTI2024

Strategies for Residential Energy Efficiency and Community Resilience for Floyd County, Kentucky

This report outlines the technical assistance provided to Floyd County Fiscal Court, Kentucky, and Vision Granted through the U.S. Department of Energy Clean Energy to Communities Expert Match Program. Floyd County, designated as “distressed” and “disadvantaged,” faces significant economic challenges, including limited job prospects, high energy costs, and youth “brain drain.” This technical assistance aims to address these pressing issues by implementing strategies to enhance home energy efficiency and livability while also aligning retrofit efforts with goals for resilience and workforce development. Through targeted guidance, this report aims to empower Floyd County residents and organizations in navigating resources for improving housing conditions, creating workforce opportunities, and enhancing resilience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Position Papers for the 2024 ASCR Workshop on Neuromorphic Computing for Science

Engineering novel neuromorphic computing systems with functionalities, capabilities, and energy efficiency similar to biological brains is one of the most exciting and challenging scientific endeavors of our time. This workshop aims to identify key research needs, challenges, and next steps necessary to develop biologically realistic neuromorphic circuits primitives that capture the functionality of neural systems found in nature. Moreover, simulating neuromorphic computing primitives integrated into networks will be key to under standing their behavior at scale, particularly for those computing architectures where full-scale commercial fabrication is not yet readily accessible. Appropriate neuroscience datasets and metrics will have to be established to vet proposed neuromorphic circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

WiDS Livermore Datathon 2025

The WiDS Datathon 2025 requires participants to build a model to predict both an individual’s sex and their ADHD diagnosis using functional brain imaging data of children and adolescents and their socio-demographic, emotions, and parenting information. The task is to create a multi-outcome model to predict two target variables: 1) ADHD (1=yes or 0=no) and 2) female (1=yes or 0=no).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Characterization of ECRAM materials and devices

As the limits of Moore’s Law approaches, new computing paradigms are developing to breakthrough this bottleneck. One such computer architecture is neuromorphic computing, which models the brain. Electrochemical random-access memory (ECRAM) is a low power and energy efficient memory due to characteristics, such as in-memory compute, ion modulation of the channel conductance, and computation distribution with large scale array integration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Autoencoding is All You Need (for MSTd)

We explore various neural network task and architectural constraints to find the minimal set that produces network activity similar to that found in primate MSTd. MSTd is a brain region in the dorsal visual pathway associated with self motion estimation from visual motion (i.e. optic flow).

Layton, Oliver W [Colby College]↗
DOE OSTI2026

A Review of Polymeric Nanoparticle Design, Synthesis, and Biomedical Applications

Polymeric nanoparticles (PNPs) are solid particles that contain macromolecular polymers ranging in size from 1-1000 nm. They have a wide variety of structures that are determined by their identity as either natural biopolymers, synthetic polymers or a combination of both. In the design of PNPs, the polymeric system, size, internal structure, additional molecules, and synthetic pathway can be engineered, allowing for a multitude of properties including differing responses to physiological environments and the incorporation of a drug. The easily accessible fine tuning of PNPs allows for a multitude of applications in medicine. PNPs allow for increased pharmacokinetics, efficacy, safety, and targeting strength when utilized as carriers for drugs. Currently there are a wide range of PNPs approved by the Food and Drug Administration for use in clinical settings and many more being investigated in clinical studies. There is also research that is being conducted for the usage of PNPs in controlled drug delivery systems and as carriers through the blood brain barrier to aid in treating neurological diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Efficient Distributed Sequence Parallelism for Transformer-Based Image Segmentation

We introduce an efficient distributed sequence parallel approach for training transformer-based deep learning image segmentation models. The neural network models are comprised of a combination of a Vision Transformer encoder with a convolutional decoder to provide image segmentation mappings. The utility of the distributed sequence parallel approach is especially useful in cases where the tokenized embedding representation of image data are too large to fit into standard computing hardware memory. To demonstrate the performance and characteristics of our models trained in sequence parallel fashion compared to standard models, we evaluate our approach using a 3D MRI brain tumor segmentation dataset. We show that training with a sequence parallel approach can match standard sequential model training in terms of convergence. Furthermore, we show that our sequence parallel approach has the capability to support training of models that would not be possible on standard computing resources.

Lyngaas, Isaac↗
DOE OSTI2025

Mapping Spiking Neural Networks to Heterogeneous Crossbar Architectures using Integer Linear Programming

Advances in novel hardware devices and architectures allow Spiking Neural Network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures (MCAs) are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear Programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1–3 orders of magnitude less solver time.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2025

SPE-CZE-MS Quantifies Zeptomole Amounts of Phosphorylated Peptides

Using 225 phosphorylated peptide standards and phosphorylated peptide-enriched mouse brain tissue, we investigate microchip-based SPE-CZE functionality, quantitative performance, and complementarity to nano-LC-MS analysis.

Coon, Joshua J. [GLBRC - University of Wisconsin]↗
DOE OSTI2022

The Association of Age-Related and Off-Target Retention with Longitudinal Quantification of [ 18 F]MK6240 Tau PET in Target Regions

6-(fluoro- 18 F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine ([ 18 F]MK6240) tau PET tracer quantifies the brain tau neurofibrillary tangle load in Alzheimer disease. The aims of our study were to test the stability of common reference region estimates in the cerebellum over time and across diagnoses and evaluate the effects of age-related and off-target retention on the longitudinal quantification of [ 18 F]MK6240 in target regions. Methods: We assessed reference, target, age-related, and off-target regions in 125 individuals across the aging and Alzheimer disease spectrum with longitudinal [ 18 F]MK6240 SUVs and SUV ratios (SUVRs) (mean ± SD, 2.25 ± 0.40 y of follow-up). We obtained SUVR from meninges, exhibiting frequent off-target retention with [ 18 F]MK6240. Additionally, we compared tracer uptake between 37 cognitively unimpaired young (CUY) (mean age, 23.41 ± 3.33 y) and 27 cognitively unimpaired older (CU) adults (amyloid-β–negative and tau-negative, 58.50 ± 9.01 y) to identify possible nonvisually apparent, age-related signal. Two-tailed t testing and Pearson correlation testing were used to determine the difference between groups and associations between changes in region uptake, respectively. Results: Inferior cerebellar gray matter SUV did not differ on the basis of diagnosis and amyloid-β status, cross-sectionally and over time. [ 18 F]MK6240 uptake significantly differed between CUY and CU adults in the putamen or pallidum (affecting ~75% of the region) and in the Braak II region (affecting ~35%). Changes in meningeal and putamen or pallidum SUVRs did not significantly differ from zero, nor did they vary across diagnostic groups. We did not observe significant correlations between longitudinal changes in age-related or meningeal off-target retention and changes in target regions, whereas changes in all target regions were strongly correlated. Conclusion: Inferior cerebellar gray matter was similar across diagnostic groups cross-sectionally and stable over time and thus was deemed a suitable reference region for quantification. Despite not being visually perceptible, [ 18 F]MK6240 has age-related retention in subcortical regions, at a much lower magnitude but topographically colocalized with significant off-target signal of the first-generation tau tracers. The lack of correlation between changes in age-related or meningeal and target retention suggests little influence of possible off-target signals on longitudinal tracer quantification. Nevertheless, the age-related retention in the Braak II region needs to be further investigated. Future postmortem studies should elucidate the source of the newly reported age-related [ 18 F]MK6240 signal, and in vivo studies should further explore its impact on tracer quantification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Experimental Oronasal Transmission of Chronic Wasting Disease Agent from White-Tailed Deer to Suffolk Sheep

Chronic wasting disease (CWD) is a fatal prion disease of cervids. We examined host range of CWD by oronasally inoculating Suffolk sheep with brain homogenate from a CWD-positive white-tailed deer. Sixty months after inoculation, 1/7 sheep had immunoreactivity against the misfolded form of prion protein in lymphoid tissue. Results were confirmed by mouse bioassay.

59 BASIC BIOLOGICAL SCIENCES↗