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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Towards Anomaly Detection at the CMS High-Level Trigger System

Traditional trigger strategies in CMS typically rely on model-dependent selections or rigid kinematic cuts, risking the omission of unexpected exotic signatures. To address this, we propose a novel anomaly detection (AD) algorithm for the High-Level Trigger (HLT), designed to serve as a complementary second layer of filtering to the Level-1 AXOL1TL AD algorithm. We employ a transformer-based foundation model trained on a diverse ensemble of Standard Model processes. By combining a joint contrastive and classification objective, and using particle kinematics as inputs, the model learns to map events to a physics-informed latent space where anomalous events are isolated from dominant backgrounds. Preliminary results show that this strategy enhances the signal-to-background ratio across a range of rare SM and BSM scenarios. Furthermore, this work constitutes foundational R&D for the potential implementation of an analogous AD algorithm in the Level-1 trigger system for Phase-2.

Cruz, Roy [U. Wisconsin, Madison (main)] (ORCID:00↗
DOE OSTI2025

Microbiome Comparison and Pathogen Identification for Three Migrating Passerines Captured During Spring Season in Jordan Using 16S rRNA Sequencing

Jordan is located on an important spot along the Mediterranean and Black Sea Flyway. Hundreds of migratory bird species have been identified stopping over in Jordan during spring and autumn migratory seasons. Compared to mammals and economically important birds, the microbiomes of wild bird species are severely understudied. Gut microbial composition is a valuable source of information that reflects food preferences, foraging behavior, and the risk of pathogen transmission to humans and other animals. In this study, we assessed the microbiome composition of three species of migrating passerines (willow warblers, lesser whitethroats, and common reed warblers) captured during the spring migration stopover in Jordan in 2023. A total of 59 fecal samples were selected evenly from the three species and subjected to 16S sequencing and microbiome analysis. Our objectives were to determine the diversity of bacteria in these three species, assess the amount of intra- and inter-specific variation, and detect pathogenic genera and species that could pose health risks to humans, domestic animals, and wildlife. Bacteria mainly belonged to the phyla Proteobacteria (62%), Actinobacteriota (18%), Firmicutes (13%), Cyanobacteria (5%), and Bacteroidota (1%). The results reveal that lesser whitethroats had the greatest variation in bacterial genus richness, Shannon diversity, and microbial composition compared to willow warblers and common reed warblers. The three bird species harbored several pathogenic genera and species, including Campylobacter, Enterococcus, Escherichia-Shigella, Mycoplasma, Rickettsia, Clostridium perfringens, and Vibrio cholerae. We suggest further investigation to understand the relationship between migratory behavior and their gut microbiome. We advocate for the use of advanced molecular techniques to characterize the pathogens found in migratory birds that might have public and environmental health impacts in addition to economic loss.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Gene-Based Detection of Microorganisms in Environmental Samples Using PCR

Contaminating microorganisms pose a serious potential risk to the crew's well being and water system integrity aboard the International Space Station (ISS). We are developing a gene-based microbial monitor that functions by replicating specific segments of DNA as much as 10(exp 12) x. Thus a single molecule of DNA can be replicated to detectable levels, and the kinetics of that molecule's accumulation can be used to determine the original concentration of specific microorganisms in a sample. Referred to as the polymerase chain reaction (PCR), this enzymatic amplification of specific segments of the DNA or RNA from contaminating microbes offers the promise of rapid, sensitive, quantitative detection and identification of bacteria, fungi, viruses, and parasites. We envision a small instrument capable of assaying an ISS water sample for 48 different microbes in a 24 hour period. We will report on both the developments in the chemistry necessary for the PCR assays to detect microbial contaminants in ISS water, and on progress towards the miniaturization and automation of the instrumentation.

Glass, John I.↗
DOE OSTI2023

Luminescent Metal–Organic Framework for the Selective Detection of Aldehydes

The detection of toxic, hazardous chemical species is an important task because they pose serious risks to either the environment or human health. Luminescent metal–organic frameworks (LMOFs) as alternative sensors offer rapid and sensitive detection of chemical species. Interactions between chemical species and LMOFs result in changes in the photoluminescence (PL) profile of the LMOFs which can be readily detected using a simple fluorometer. Herein, we report the use of a robust, Zn-based LMOF, [Zn 5 (μ 3 -OH) 2 (adtb) 2 (H 2 O) 5 ·5 DMA] (Zn-adtb, LMOF-341), for the selective detection of benzaldehyde. Upon exposure to benzaldehyde, Zn-adtb experiences significant luminescent quenching, as characterized through PL experiments. Photoluminescent titration experiments reveal that LMOF-341 has a detection limit of 64 ppm and a Ksv value of 179 M –1 for benzaldehyde. Furthermore, we study the guest–host interactions that occur between LMOF-341 and benzaldehyde through in situ Fourier transform infrared and computational modeling employing density functional theory. The results show that benzaldehyde interacts more strongly with LMOF-341 compared to formaldehyde and propionaldehyde. Furthermore, our combined studies also reveal that the mechanism of luminescence quenching originates from an electron-transfer process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗
NASA NTRS2000

A Nuclear Interaction Model for Understanding Results of Single Event Testing with High Energy Protons

An internuclear cascade and evaporation model has been adapted to estimate the LET spectrum generated during testing with 200 MeV protons. The model-generated heavy ion LET spectrum is compared to the heavy ion LET spectrum seen on orbit. This comparison is the basis for predicting single event failure rates from heavy ions using results from a single proton test. Of equal importance, this spectra comparison also establishes an estimate of the risk of encountering a failure mode on orbit that was not detected during proton testing. Verification of the general results of the model is presented based on experiments, individual part test results, and flight data. Acceptance of this model and its estimate of remaining risk opens the hardware verification philosophy to the consideration of radiation testing with high energy protons at the board and box level instead of the more standard method of individual part testing with low energy heavy ions.

Culpepper, William X.↗
DOE OSTI2024

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Innovative approach to counterfeit and noncompliant refrigerant detection: A cost-effective, portable solution

The increasing prevalence of counterfeit and incompatible refrigerants presents significant risks to Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) systems, including compromised equipment performance, safety hazards, and non-compliance. This article details the development of a novel, cost-effective, and portable detection device designed to accurately verify refrigerants. The device utilizes a controlled gas sampling and analysis system within a sealed chamber, ensuring precise measurements while maintaining safety through a purging mechanism. The system features a high-sensitivity sensor integrated with an onboard control module that analyzes gas composition in real-time, providing feedback within a 2-minute duration. Laboratory validation demonstrated the device’s high accuracy (>95 % based on correct identification of compliant vs. non-compliant blends) in detecting unauthorized refrigerant blends. The projected cost of the product stands at ∼ $150, based on the retail pricing of individual components. Laboratory validation demonstrated the device’s high accuracy (>95 % for composition identification, 100 % rejection of tested counterfeit/incorrect blends) in detecting unauthorized refrigerant blends with a response time <2 min. The device correctly identified authentic R-454A/B/C blends and reliably rejected R-407F and closely related counterfeit mixtures. Key advantages include affordability, ease of use, rapid response time, and compatibility with a wide range of refrigerants. This solution supports compliance with regulatory frameworks, enhances safety in HVAC&R operations, and mitigates the risks associated with counterfeit refrigerants.

Counterfeit refrigerants↗
NASA NTRS2008

CEV OME/RCS Flow Health Monitor: Status Review

I. Primary purpose: detect propellant valve leakage: a) Reduce launch propellant mass by reducing leakage loss . margins, b) Improve safety by reducing risk of propellant ice build up in thruster. II. Secondary objectives: a) Wetness sensor to detect that lines have been flooded. b) Monitor engine performance (timing, mix ratio). c) Use in GSE as valve leakage monitor.

Youngquist, Robert↗
DOE OSTI2020

Chemical Impacts of Potential CO2 and Brine Leakage on Groundwater Quality with Quantitative Risk Assessment: A Case Study of the Farnsworth Unit

Potential leakage of reservoir fluids is considered a key risk factor for geologic CO2 sequestration (GCS), with concerns of their chemical impacts on the quality of overlying underground sources of drinking water (USDWs). Effective risk assessment provides useful information to guide GCS activities for protecting USDWs. In this study, we present a quantified risk assessment case study of an active commercial-scale CO2-enhanced oil recovery (CO2-EOR) and sequestration field, the Farnsworth Unit (FWU). Specific objectives of this study include: (1) to quantify potential risks of CO2 and brine leakage to the overlying USDW quality with response surface methodology (RSM); and (2) to identify water chemistry indicators for early detection criteria. Results suggest that trace metals (e.g., arsenic and selenium) are less likely to become a risk due to their adsorption onto clay minerals; no-impact thresholds based on site monitoring data could be a preferable reference for early groundwater quality evaluation; and pH is suggested as an indicator for early detection of a leakage. This study may provide quantitative insight for monitoring strategies on GCS sites to enhance the safety of long-term CO2 sequestration.

58 GEOSCIENCES↗
DOE OSTI2021

Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis

Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2022

Un-Numbered Operational Areas PRL 229 Railroad Tie Disposal Area Pond Confirmation Sampling Report

This Confirmatory Sampling (CS) Report (CSR) was prepared for the National Aeronautics and Space Administration (NASA), Kennedy Space Center (KSC), Florida in accordance with the CS Work Plan (CSWP) (HGL, 2022a). The CSR summarizes investigation activities performed on the Railroad Tie Disposal Area (RTDA) of the Un-Numbered Operational Areas (UNOA), Potential Release Location (PRL) 229 located at KSC. HydroGeoLogic, Inc. (HGL) prepared this report under Contract Number 80KSC019D0012. The purpose of the CSR is to provide information to evaluate the level of potential contamination in surface water and sediment samples collected from the RTDA PRL 229 Pond and determine if contaminants pose unacceptable risks to ecological receptors. The CSR provides analytical data of 10 sets of surface water and sediment samples collected in 2021. Surface water samples were analyzed for volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), polynuclear aromatic hydrocarbon (PAHs), metals, hardness, and salinity, while sediment samples were analyzed for metals, total organic carbon (TOC), SVOCs, PAHs, and total petroleum hydrocarbons (TPH). The CSR includes a Screening Level Ecological Risk Assessment (SLERA) conducted under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program to determine if unacceptable risks to ecological receptors exist at PRL 229 Pond. Metals and PAHs were detected in surface water samples, including detections of antimony, arsenic, barium, calcium, copper, lead, magnesium, thallium, zinc, anthracene, benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(g,h,i)perylene, benzo(k)fluoranthene, chrysene, dibenzo(a,h)anthracene, fluoranthene, indeno(1,2,3-c,d)pyrene and pyrene. Based on measured salinity (1.03 to 1.21 parts per thousand), the pond was identified as freshwater. Sediment analytical results also contained detections of metals and PAHs, including antimony, arsenic, barium, beryllium, cadmium, chromium, copper, lead, mercury, nickel, selenium, thallium, zinc, anthracene benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(g,h,i)perylene, benzo(k)fluoranthene, chrysene, dibenzo(a,h)anthracene, fluoranthene, indeno(1,2,3-c,d)pyrene and pyrene. Potential risks to ecological receptors were evaluated in accordance with the SLERA Work Plan and the Decision Process Document for the Resource Conservation and Recovery Act (RCRA) Corrective Action Program at KSC (DPD) (Geosyntec, 2019), and using the 2021 sediment and surface water analytical results. Potential ecological receptors evaluated in the SLERA include the aquatic community, benthic invertebrates, aquatic birds, and aquatic mammals. The SLERA included Steps 2a through 2c of the Ecological Risk Assessment (ERA) process in accordance with the DPD. Using multiple lines of evidence, the SLERA concluded that many of the detected analytes are present at concentrations similar to those in background or reference locations at KSC, and/or at concentrations that would not pose a significant risk to ecological communities. There are no screening values available for the avian community, and PAHs are bioaccumulative compounds. For this reason, food web modeling was completed as a supplement to Step 2c to provide a quantitative evaluation of this exposure pathway. Food web modeling indicates that PAHs are unlikely to pose a risk to birds exposed to site sediment and surface water. Based on the results of the SLERA, the contaminants in sediment and surface water pose no to minimal risk to the ecological communities that could live or forage at the site. Based on the results of the SLERA, no further study is recommended for the RTDA Pond at PRL 229.

Confirmation Sampling↗
NASA NTRS2022

Lake Champlain Water Resources: Using Earth Observations to Identify Spatial and Seasonal Trends of Harmful Algal Events in Lake Champlain

Lake Champlain provides clean drinking water for 35% of the surrounding watershed and offers recreational opportunities to millions of tourists. However, current levels of cyanobacteria and phosphorus created breeding grounds for harmful algal blooms (HABs). The excess of phosphorus runoff into Lake Champlain over the past decade encouraged toxic cyanobacterial formations, thereby increasing the severity of HABs towards local economy and ecology. In partnership with the Natural Resources Conservation Service (NRCS) Northeast Region, this project utilized Earth observations to identify risk factors associated with toxic algal blooms. The team detected historic algal bloom trends with Sentinel-2 Multispectral Instrument (MSI), Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 Operational Land Imager (OLI) and Landsat 9 OLI-2. The team also used Sentinel-3 OLCI and the German Aerospace Center’s Earth Sensing Imagery Spectrometer (DESIS) to visualize algal bloom patterns and Landsat 8 OLI, Landsat 9 OLI-2, and Shuttle Radar Topography Mission (SRTM) to identify phosphorus sources within the watershed. The team’s analyses indicated an increase in cyanobacteria blooms during summer months from 2016-2022, with Missisquoi and St. Albans Bay exhibiting the greatest concentrations of toxic events. Furthermore, 16% of the watershed was identified as posing an immediate threat to the lake’s hydrology. The area of greatest concern was the Missisquoi Bay sub-watershed, with 229,044 acres of land prone to excessive phosphorus runoff. Providing this information to the NRCS Northeast Region enabled the organization to quantify risk factors associated with algal blooms and modify mitigation efforts to better target future bloom events.

Brianne Kendall↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Smoke detection in low-G fires

Fires in spacecraft are considered a credible risk. To respond to this risk, NASA flew fire detectors on Skylab and the Space Shuttle (STS) and included them in the design for International Space Station Alpha (ISSA). In previous missions (Mercury, Gemini and Apollo), the crew quarters were so cramped that it was not considered credible that the astronauts could fail to observe a fire. The Skylab nodule included approximately 20 UV fire detectors. The space shuttle has 9 ionization detectors in the mid deck and flight deck and Spacelab has six additional ionization detectors. The planned detectors for ISSA are laser-diode, forward-scattering, smoke or particulate detectors. Current plans for the ISSA call for two detectors in the open area of the module and detectors in racks that have both cooling air flow and electrical power. Due to the complete absence of data concerning the nature of particulate and radiant emission from low-g fires, all three of these detector systems were designed based upon 1-g test data. As planned mission durations and complexity increase and the volume of spacecraft increases, the need for and importance of effective, crew independent, fire detection grows significantly. This requires more knowledge concerning low-gravity fires and how they might be detected. To date, no combustion-generated particulate samples have been collected for well-developed microgravity flames. All of the extant data come from drop tower tests and therefore only correspond to the early stages of a fire. The fuel sources were restricted to laminar gas-jet diffusion flames and rapidly overheated wire insulation. These gas-jet drop tower tests indicate, through thermophoretic sampling, that soot primaries and aggregates (groups of primary particles) in micro-g may be significantly larger than those in normal-g (ng). This raises new scientific questions about soot processes as well as practical issues for particulate detection/alarm threshold levels used in on-orbit smoke detectors. Furthermore, it is widely speculated but unverified that the aggregates will grow to very large scales in a microgravity fire of longer duration than available on the ground. Preliminary tests in the 2.2 second drop tower suggest that particulate generated by overheated wire insulation will also be larger in microgravity than in normal gravity. TEM grids downstream of the fire region in the WIF experiment as well as visual observation of long string-like aggregates, further confirm this suggestion. The combined impact of these limited results and theoretical predictions is that direct knowledge of low-g combustion particulate as opposed to extrapolation from 1-g data is needed for a more confident design of smoke detectors for spacecraft.

Urban, David L.↗
NASA NTRS2003

"Smoke": Characterization Of Smoke Particulate For Spacecraft Fire Detection

The "Smoke" experiment is a flight definition investigation that seeks to increase our understanding of spacecraft fire detection through measurements of particulate size distributions of preignition smokes from typical spacecraft materials. Owing to the catastrophic risk posed by even a very small fire in a spacecraft, the design goal for spacecraft fire detection is to detect the fire as quickly as possible, preferably in the preignition phase before a real flaming fire has developed. Consequently the target smoke for detection is typically not soot (typical of established hydrocarbon fires) but instead, pyrolysis products, and recondensed polymer particles. At the same time, false alarms are extremely costly as the crew and the ground team must respond quickly to every alarm. The U.S. Space Shuttle (STS: Space Transportation System) and the International Space Station (ISS) both use smoke detection as the primary means of fire detection. These two systems were designed in the absence of any data concerning low-gravity smoke particle (and background dust) size distributions. The STS system uses an ionization detector coupled with a sampling pump and the ISS system is a forward light scattering detector operating in the near IR. These two systems have significantly different sensitivities with the ionization detector being most sensitive (on a mass concentration basis) to smaller particulate and the light scattering detector being most sensitive to particulate that is larger than 1 micron. Since any smoke detection system has inherent size sensitivity characteristics, proper design of future smoke detection systems will require an understanding of the background and alarm particle size distributions that can be expected in a space environment.

Urban, David L.↗
NASA NTRS2020

In-Flight Detection of Vibration Anomalies in Unmanned Aerial Vehicles

Owing to the frequency of occurrence and high risk associated with bearings, identification and characterization of bearing faults in motors via nondestructive evaluation (NDE) methods have been studied extensively, amongst which vibration analysis has been found to be a promising technique for early diagnosis of anomalies. However, a majority of the existing techniques rely on vibration sensors attached onto or in close proximity to the motor in order to collect signals with a relatively high SNR. Due to weight and space restrictions, these techniques cannot be used in unmanned aerial vehicles (UAVs), especially during flight operations since accelerometers cannot be attached onto motors in small UAVs. Small UAVs are often subjected to vibrational disturbances caused by multiple factors such as weather turbulence, propeller imbalance or bearing faults. Such anomalies may not only pose risks to UAV's internal circuitry, components or payload, they may also generate undesirable noise level particularly for UAVs expected to fly in low-altitudes or urban canyon. This paper presents a detailed discussion of challenges in in-flight detection of bearing failure in UAVs using existing approaches and offers potential solutions to detect overall vibration anomalies in small UAV operations based on IMU data.

Portia Banerjee↗
DOE OSTI2021

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

Background: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Methods: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. Results: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. Conclusions: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

60 APPLIED LIFE SCIENCES↗