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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CACNA1D is a circadian gene and causes familial advanced sleep phase

Familial advanced sleep phase (FASP) is a heritable human sleep trait characterized by early sleep onset and offset times. We have identified five variants in five different families in the human voltage-gated calcium channel subunit alpha1 D (CACNA1D) that cosegregate with FASP. The variants inCACNA1Dlead to altered channel dynamics in vitro. A mouse model of the E427K variant has a normal circadian period under constant darkness but displays altered phase shifts in response to light in the subjective night at circadian time (CT) 16 and CT22. Overall, these experiments establishCACNA1Das an FASP gene with altered entrainment, highlighting the ability of human genetics to uncover novel aspects of human circadian regulation.

Science & Technology - Other Topics↗
DOE OSTI2024

Modeling Framework for the Assessment of a Sustainable Hydrogen Production and Supply Chain Network in California

The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.

Zaki, Mohammed Tamim↗
DOE OSTI2025

Hantavirus is Associated With Open Developed Areas and Arid Climates, Highlighting Increased Risk in the Western United States

In the United States, hantaviruses can cause hantavirus pulmonary syndrome (HPS) in humans, an acute respiratory illness with a high mortality rate. Most people contract HPS from exposure to infected rodent excrement. The interannual dynamics of hantavirus transmission are tied to both environmental and human-related factors, including changes in annual climate conditions, rodent populations, and the built environment in which humans are more likely to be exposed. Similar environmental conditions and socioeconomic factors also likely determine the long-term risk of hantavirus exposure. Here, we use ecological niche models and human cases of HPS in the U.S. from 1993 to 2022 to assess hantavirus risk using four socioeconomic variables, 17 land use variables, one variable of rodent richness, and seven climate variables to determine both the geographical locations of highest exposure risk and leading environmental predictors. We found that areas with higher relative risk tend to be where it is drier, higher social vulnerability, increased rodent richness, and more open to low levels of development—this largely mapped to the western U.S. We found evidence that fringe ecosystems may be important areas of hantavirus transmission, similar to other emerging diseases. Increased rodent richness was associated with increased hantavirus risk, warranting further investigation into how the abundance and community composition of rodents could impact long-term risk. These risk maps can help public health officials develop plans for mitigating hantavirus, especially for the most susceptible populations. They can also be used to further investigate regions estimated to be at high risk for hantavirus where disease cases have not been as common but may be underreported.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024

Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Host population dynamics influence Leptospira spp. transmission patterns among Rattus norvegicus in Boston, Massachusetts, US

Leptospirosis (caused by pathogenic bacteria in the genus Leptospira ) is prevalent worldwide but more common in tropical and subtropical regions. Transmission can occur following direct exposure to infected urine from reservoir hosts, or a urine-contaminated environment, which then can serve as an infection source for additional rats and other mammals, including humans. The brown rat, Rattus norvegicus , is an important reservoir of Leptospira spp. in urban settings. We investigated the presence of Leptospira spp. among brown rats in Boston, Massachusetts and hypothesized that rat population dynamics in this urban setting influence the transportation, persistence, and diversity of Leptospira spp. We analyzed DNA from 328 rat kidney samples collected from 17 sites in Boston over a seven-year period (2016–2022); 59 rats representing 12 of 17 sites were positive for Leptospira spp. We used 21 neutral microsatellite loci to genotype 311 rats and utilized the resulting data to investigate genetic connectivity among sampling sites. We generated whole genome sequences for 28 Leptospira spp. isolates obtained from frozen and fresh tissue from some of the 59 positive rat kidneys. When isolates were not obtained, we attempted genomic DNA capture and enrichment, which yielded 14 additional Leptospira spp. genomes from rats. We also generated an enriched Leptospira spp. genome from a 2018 human case in Boston. We found evidence of high genetic structure among rat populations that is likely influenced by major roads and/or other dispersal barriers, resulting in distinct rat population groups within the city; at certain sites these groups persisted for multiple years. We identified multiple distinct phylogenetic clades of L. interrogans among rats that were tightly linked to distinct rat populations. This pattern suggests L. interrogans persists in local rat populations and its transportation is influenced by rat population dynamics. Finally, our genomic analyses of the Leptospira spp. detected in the 2018 human leptospirosis case in Boston suggests a link to rats as the source. These findings will be useful for guiding rat control and human leptospirosis mitigation efforts in this and other similar urban settings.

Stone, Nathan E.↗
DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Application of Artificial Intelligence/Machine Learning to Operations Research

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Robotic Assembly of SRF Cavity Pair

Superconducting Radio Frequency (SRF) cavities for particle accelerators require tight tolerances, ultrahigh vacuum, and strict cleanliness during assembly. As in the semiconductor industry, defects such as particles and residues are deleterious to performance, possibly rendering a cavity unfit for use. This problem is addressed primarily through cleanroom assembly during sensitive steps and rigorous chemical processing to prevent and remove such defects. Human workers are often the largest source of contamination, even with proper gowning and practices. The semiconductor industry has long integrated robots in cleanroom operation, but this has not occurred for SRF cavity production; SRF cavities, unlike wafers, are complex shapes, require more hands-on mechanical assembly, and are low-volume production items. At Jefferson Laboratory, a co-operative robot (cobot) has been setup to overcome these problems. Cobots are safe for use alongside human workers and can integrate new tools such as a 3D camera part detection and gripper for item manipulation. Therefore, cobots represent a promising avenue for reducing particulate generated during a variety of assembly tasks. A mockup of a cavity pair and coupler was setup and the cobot programmed to automatically pick up the coupler and place on the mating cavity flange. Particle counting methods were setup to measure human vs cobot assembly particulate generation inside a cavity mockup. Other potential uses will be discussed for further improving SRF cavity assembly steps, where a cobot can replace or assist a human operator, and what potential gains are expected.

Duzik, Adam [Thomas Jefferson National Accelerator↗
DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗
DOE OSTI2025

The Impact of Cultural Values and Organizational Processes on Nuclear Security Operations

Human performance is a pivotal factor in the design, testing, maintenance, and operation of security systems. The effectiveness of these systems relies not only on the capabilities, limitations, motives, and attitudes of the individuals involved, but also on the quality of training, instructional content, and evaluation methods provided. To uphold security standards, seamless integration between technologies and operators necessitates reliable human input. In security operations, human errors, often attributed to blame, sanctions, low motivation, individual accountability, or complacency, are primary causes of system failures. Complacency, characterized by a false sense of security, reflects a lack of awareness of potential threats and is a significant contributing factor to lapses in security. Security incidents arise from various factors, many extend beyond individual control, highlighting the need for a holistic approach to human performance that integrates organizational processes and team collaboration. Historically, errors have been attributed to individual moral or cognitive failures. However, insights from Operational Experiences (OEs) suggest that organizational processes weakness and deficiencies in nuclear cultural values contribute more significantly to security failures than individual mistakes. This paper consolidates lessons learned from diverse international nuclear security cultures and aims to highlight the importance of security culture in shaping global perspectives on nuclear security. It underscores the role of cultural values in shaping nuclear security practices and enhancing the resilience of security systems in the nuclear sector.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Increased inflammation as well as decreased endoplasmic reticulum stress and translation differentiate pancreatic islets from donors with pre-symptomatic stage 1 type 1 diabetes and non-diabetic donors

Aims/hypothesis Progression to type 1 diabetes is associated with genetic factors, the presence of autoantibodies and a decline in beta cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting beta cells prior to the onset of type 1 diabetes. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet-autoantibody-positive organ donors with pre-symptomatic stage 1 type 1 diabetes (HbA1c ≤42 mmol/mol [6.0%]). We aimed to identify pathways in islets that are indicative of beta cell dysfunction. Methods Multiple islet sections were collected through laser microdissection of frozen pancreatic tissues from organ donors positive for single or multiple islet autoantibodies (AAb + , n=5), and age (±2 years)- and sex-matched non-diabetic (ND) control donors (n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to MS-based proteomics and analysed with label-free quantification followed by pathway and functional annotations. Results Analyses resulted in ~4500 proteins identified with low false discovery rate (<1%), with 2165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on only the donors with stage 1 type 1 diabetes who were positive for multiple autoantibodies (mAAb + , n=3) and genetic risk compared with their matched ND controls (n=3) for the final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different (unadjusted p<0.025, q<0.15) for mAAb + vs ND donor islets. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including a proteomics dataset from in vitro proinflammatory cytokine-treated human islets and single-cell RNA-seq datasets from AAb + individuals. Conclusions/interpretation In situ human islet proteome alterations in stage 1 type 1 diabetes centred around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation/HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on beta cell changes during type 1 diabetes progression and pathogenesis. Data availability The LC-MS raw datasets that support the findings of this study have been deposited in the online repository: MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) with accession no. MSV000090212.

Autoantibody-positive↗
DOE OSTI2025

PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI

Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. Here, this paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: https://github.com/hrlblab/PHASE.

Deep learning↗
DOE OSTI2025

The development and evolution of biological AMS at Livermore: a perspective

Biological accelerator mass spectrometry (AMS) provides ultrasensitive carbon-14 isotopic analysis enabling a deeper understanding of human health concerns by enabling quantification of pharmacokinetics and other molecular endpoints directly in humans. It enables environmentally and human relevant studies of metabolic pathways through the use of very low concentrations of labeled metabolic substrates in cells and organisms. Here, we discuss why AMS is an important tool for the biosciences, the development and evolution of biological AMS at Livermore and discuss technical refinements that will improve the efficiency of operation for the measurement of ultra-trace levels of 14 C, which, long term, will enable greater ease of use and sample throughput.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

YAP1 Dysfunction Promotes Molecular Properties Linked to Breast Cancer Susceptibility

YAP1 is a cotranscription factor that promotes malignant and stem cell properties in cancer. We previously found that YAP1 dysregulation is associated with aging in human mammary epithelia. With increased age, YAP1 expression changes in luminal epithelial cells, the prospective breast cancer cell of origin. Because age is a significant risk factor for breast cancer, we tested whether YAP1 dysregulation acted early in cancer progression by conferring cellular states associated with increased cancer susceptibility. In this study, we find that with increased age and genetic risk for developing cancer, human breast tissues showed significantly increased YAP1 expression, and cultured primary human mammary epithelial cells (HMEC) showed significantly increased expression of both YAP1 and its transcriptional targets. Increased YAP1 expression in cultured HMEC induced gene expression changes associated with increased cancer susceptibility, such as genes associated with stem cell states, increased telomerase activity, breast cancer progression, and increased age and genetic breast cancer risk. Furthermore, overexpression of YAP1 in post-stasis HMEC—finite lifespan cells that have bypassed a retinoblastoma-mediated senescence barrier—promoted properties related to increased growth potential. We found that YAP1 dysregulation in finite epithelial cells allows for access to gene programs and functions that are typically thought to be restricted to stem cells. We hypothesize that YAP1 acts early in breast cancer progression, long before the development of a tumor, to impose cancer-susceptible molecular states.

YAP1↗
DOE OSTI2025

Review of Reactor Facilities without Main Control Rooms

Small, advanced reactors may require few, if any, safety-related human actions (HAs) and fewer HAs to monitor and control the plant. In comparison to large light water reactors, these are significant changes that have implications for many aspects of an applicant's human factors engineering (HFE), including control room design, plant staffing, and the management of safety functions. To support the Nuclear Regulatory Commission's (NRC) ability to evaluate these changes, information needs to be developed addressing the characteristics and potential issues. The objectives of our research were to (1) identify when a traditional main control room (MCR) may not be necessary, (2) identify workplace design alternatives to traditional MCRs, and (3) develop guidance for reviewing an applicant's workplace designs with and without a MCR. We determined that the safety question isn't so much justifying why a design has no MCR, but rather verifying that important human actions can be accurately and reliably performed under a range of challenging conditions using the HSIs provided regardless of their location. We developed guidance to review alternatives to MCRs based on HFE analyses for determining workplace location and design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Brain phosphoproteomic analysis identifies diabetes‐related substrates in Alzheimer's disease pathology in older adults

INTRODUCTION: Type 2 diabetes increases the risk of Alzheimer's disease (AD) dementia. Insulin signaling dysfunction exacerbates tau protein phosphorylation, a hallmark of AD pathology. However, the comprehensive impact of diabetes on patterns of AD-related phosphoprotein in the human brain remains underexplored. METHODS: We performed tandem mass tag–based phosphoproteome profiling in post mortem human brain prefrontal cortex samples from 191 deceased older adults with and without diabetes and pathologic AD. RESULTS: Among 7874 quantified phosphosites, microtubule-associated protein tau (MAPT) phosphorylated at T529 and T534 (isoform 8 T212 and T217) were more abundant in AD and showed differential associations with diabetes. Network analysis of co-abundance patterns uncovered synergistic interactions between AD and diabetes, with one module exhibiting higher MAPT phosphorylation (15 MAPT phosphosites) and another displaying lower MAP1B phosphorylation (22 MAP1B phosphosites). DISCUSSION: This study offers phosphoproteomics insights into AD in diabetes, shedding light on mechanisms that can inform the development of therapeutics for dementia. Highlights: The risk of Alzheimer's disease (AD) dementia is increased among older adults living with diabetes. The patterns of AD-related phosphoprotein in the human brain in older adults are differential among older adults living with diabetes. Microtubule-associated protein tau phosphorylated at T529 and T534 (isoform 8 T212 and T217) showed differential associations with diabetes. Phosphosite co-abundance networks of synergistic interactions between AD and diabetes were identified.

60 APPLIED LIFE SCIENCES↗