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DOE OSTI2025

Robust measurement of microbial reduction of graphene oxide nanoparticles using image analysis

ABSTRACT Shewanella oneidensis ( S. oneidensis ) has the capacity to reduce electron acceptors within a medium and is thus used frequently in microbial fuel generation, pollutant breakdown, and nanoparticle fabrication. Microbial fuel setups, however, often require costly or labor-intensive components, thus making optimization of their performance onerous. For rapid optimization of setup conditions, a model reduction assay can be employed to allow simultaneous, large-scale experiments at lower cost and effort. Since S. oneidensis uses different extracellular electron transfer pathways depending on the electron acceptor, it is essential to use a reduction assay that mirrors the pathways employed in the microbial fuel system. For microbial fuel setups that use nanoparticles to stimulate electron transfer, reduction of graphene oxide provides a more accurate model than other commonly used assays as it is a bulk material that forms flocculates in solutions with a large ionic component. However, graphene oxide flocculates can interfere with traditional absorbance-based measurement techniques. This study introduces a novel image analysis method for quantifying graphene oxide reduction, showing improved performance and statistical accuracy over traditional methods. A comparative analysis shows that the image analysis method produces smaller errors between replicates and reveals more statistically significant differences between samples than traditional plate reader measurements under conditions causing graphene oxide flocculation. Image analysis can also detect reduction activity at earlier time points due to its use of larger solution volumes, enhancing color detection. These improvements in accuracy make image analysis a promising method for optimizing microbial fuel cells that use nanoparticles or bulk substrates. IMPORTANCE Shewanella oneidensis ( S. oneidensis ) is widely used in reduction processes such as microbial fuel generation due to its capacity to reduce electron acceptors. Often, these setups are labor-intensive to operate and require days to produce results, so use of a model assay would reduce the time and expenses needed for optimization. Our research developed a novel digital analysis method for analysis of graphene oxide flocculates that may be utilized as a model assay for reduction platforms featuring nanoparticles. Use of this model reduction assay will enable rapid optimization and drive improvements in the microbial fuel generation sector.

Bennett, Danielle T. (ORCID:0009000188748827)↗

NASA NTRSDate not supplied

Combining RNA-SEQ Datasets from NASA GENELAB: An Evaluation of Correction Methods

Background: Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. Methods: In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, the median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. Results: The results showed that the reference-based approach introduced several additional (and likely artificial) differentially expressed genes when compared with the respective standard approach. Conclusions: Of the methods tested, standard ComBat_seq and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

Finsam Samson↗

DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

The Past, Present and Future of Structural Health Monitoring: An Overview of Three Ages

This paper presents an overview of the discipline of structural health monitoring (SHM), organised in terms of three proposed ages. The first age is delineated by the prehistory of SHM and the period where nondestructing testing methods evolved into an organised set of principles built upon physics-based models; this age ended when the model-based approaches reached an impasse in terms of their ability to properly deal with real-world problems. The second age of SHM began with a transition to data-based methods based on statistical pattern recognition, which provided a holistic approach to SHM problems for the first time. This age arguably ended when the methods foundered in situations where the necessary training data were scarce. It is argued here that the third age began with the development of population-based SHM, which has been designed to overcome the problem of data scarcity. As there is very limited space in a single article to provide a comprehensive overview, an appendix has been provided here that gives a very systematic bibliography of SHM reviews—a meta-bibliography.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1965

Determination of the survival probability of a launch vehicle rising through a random wind field

Survival probability of rising launch vehicle subjected to wind shear loads - statistical load survey method, and wind profile analyses for design considerations

STATISTICAL PROBABILITY↗

NASA NTRS2011

MIRO Continuum Calibration for Asteroid Mode

MIRO (Microwave Instrument for the Rosetta Orbiter) is a lightweight, uncooled, dual-frequency heterodyne radiometer. The MIRO encountered asteroid Steins in 2008, and during the flyby, MIRO used the Asteroid Mode to measure the emission spectrum of Steins. The Asteroid Mode is one of the seven modes of the MIRO operation, and is designed to increase the length of time that a spectral line is in the MIRO pass-band during a flyby of an object. This software is used to calibrate the continuum measurement of Steins emission power during the asteroid flyby. The MIRO raw measurement data need to be calibrated in order to obtain physically meaningful data. This software calibrates the MIRO raw measurements in digital units to the brightness temperature in Kelvin. The software uses two calibration sequences that are included in the Asteroid Mode. One sequence is at the beginning of the mode, and the other at the end. The first six frames contain the measurement of a cold calibration target, while the last six frames measure a warm calibration target. The targets have known temperatures and are used to provide reference power and gain, which can be used to convert MIRO measurements into brightness temperature. The software was developed to calibrate MIRO continuum measurements from Asteroid Mode. The software determines the relationship between the raw digital unit measured by MIRO and the equivalent brightness temperature by analyzing data from calibration frames. The found relationship is applied to non-calibration frames, which are the measurements of an object of interest such as asteroids and other planetary objects that MIRO encounters during its operation. This software characterizes the gain fluctuations statistically and determines which method to estimate gain between calibration frames. For example, if the fluctuation is lower than a statistically significant level, the averaging method is used to estimate the gain between the calibration frames. If the fluctuation is found to be statistically significant, a linear interpolation of gain and reference power is used to estimate the gain between the calibration frames.

Lee, Seungwon↗

NASA NTRS1993

Parameter estimation techniques based on optimizing goodness-of-fit statistics for structural reliability

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell's numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper's nonlinear regression algorithm.

Starlinger, Alois↗

NASA NTRS2025

A Minority Report Submitted as an Addendum to the Report of the Mars 2020 Organic Contamination Panel

This Minority Report (MR) presents seven findings in addition to or contrary to the main OCP report: 1. Contamination control for the Mars 2020 cache must be strict. Mars’ surface is known to be organics-poor from laboratory studies, Mars meteorite analyses, and from four previous NASA missions. It is imperative that contamination control measures are enacted that enable reliable and robust detection of potential biomarker compounds at the ppb level. 2. Since Mars 2020 is a sample return mission and analyses of samples in the returnable cache are expected to occur after return to Earth, positive controls are not recommended for flight on Mars 2020 unless a compelling case can be made for their use. 3. The findings of previous panels dedicated to organic compound analysis in Martian samples (OCSSG, ND-SAG, SDT) recommend TOC limits between 10-40 ppb. The MR finds that the lower limit of 10 ppb is recommended and that insufficient justification is given by the OCP Panel Report (PR) to raise the TOC limit to 40 ppb. 4. Analytical capability is sufficiently advanced that analytical capability is an irrelevant consideration with respect to differentiating between 10 and 40 ppb TOC. 5. Perceived contamination control challenges are an irrelevant consideration for raising the TOC contamination limit from 10 to 40 ppb since those challenges, and the procedures to ameliorate them, will exist regardless of whether the limit is 10 or 40 ppb. 6. The “dilution cleaning” method has not been adequately proven for utilization on the Mars 2020 mission. Shortcomings have been identified in terms of peer review, method verification, analytical and testing approach, application to space flight hardware, and performance under Martian conditions. The method should be revisited and independently tested using statistically and analytically robust methods, and scrutinized in a rigorous peer review process. 7. The Mars 2020 mission claims considerable heritage from the Mars Science Laboratory (MSL) mission, but MSL contamination control efforts contain significant errors and implementation discrepancies that would imperil the Mars 2020 caching mission were they repeated. A standing contamination control panel should be formed to provide independent oversight for the Mars 2020 mission.

Mars Sample Return↗

NASA NTRS1989

Transmission overhaul and replacement predictions using Weibull and renewel theory

A method to estimate the frequency of transmission overhauls is presented. This method is based on the two-parameter Weibull statistical distribution for component life. A second method is presented to estimate the number of replacement components needed to support the transmission overhaul pattern. The second method is based on renewal theory. Confidence statistics are applied with both methods to improve the statistical estimate of sample behavior. A transmission example is also presented to illustrate the use of the methods. Transmission overhaul frequency and component replacement calculations are included in the example.

Savage, M.↗

NASA NTRS1989

Transmission overhaul and replacement predictions using Weibull and renewal theory

Savage, M.↗

NASA NTRSDate not supplied

Estimating Bidirectional Reflectance and Monitoring Stability of SNPP-VIIRS Reflective Solar Bands Using A Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System project provides the scientific community with observed top-of-atmosphere shortwave and longwave fluxes for climate monitoring and climate model validation. To provide consistent VIIRS cloud retrievals, the CERES Imager and Geostationary Calibration Group (IGCG) must understand and quantify the stability of the VIIRS instruments. To achieve this, the IGCG utilizes tropical deep convective clouds (DCCs) as invariant targets. Proper seasonal characterization of the DCC bidirectional reflectance distribution function (BRDF) is key to the success of DCC-based calibration methods, particularly for shortwave infrared (SWIR) bands. This article proposes the use of a deep neural network (DNN) to characterize VIIRS solar reflective band BRDF reflectance, with which individual channel trends are isolated by manipulating the DNN time input. Initial results show that the DNN method can extract statistically significant SNPP-VIIRS band trends, using only SNPP-VIIRS inputs, that are correlative to and match the magnitude of significant trends determined using methods that rely on an external angular distribution model. It may be possible to apply this approach to actively monitor the stability of new instruments without the need for predetermined seasonal BRDF corrections.

Benjamin Scarino↗

DOE OSTI2026

Measurements of three-flavor neutrino oscillations from a PISCES two-detector fit to the NOvA Experiment data

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.

Rajaoalisoa, Miriama [Cincinnati U.]↗

NASA NTRS1985

Evaluation of fault-tolerant system performance by approximate techniques

An approximate method for calculating the statistics of the performance of a fault-tolerant system is developed. An approximate method is necessary because the statistical model of the system behavior is large-scale and the time horizon of interest encompasses many cycles of the Redundancy Management logic. In the development, a compact representation of the necessary information called the v-transform is introduced and discussed. Based upon this representation, an approximation that leads to a very efficient computational procedure is suggested and numerically analyzed. A very brief discussion of other related work is also presented.

Walker, B. K.↗

NASA NTRS1988

Detection of road boundary

Several researchers have proposed and implemented various systems pertaining to the development of autonomous land vehicles (ALVs). One fundamental problem associated with the navigation of an ALV is the ability to efficiently extract the boundaries of the pathway that need to be navigated. In this paper a method is presented that will determine the road boundaries in one pass using a limited search area in the input image. The method employs the statistical information regarding the gray levels present in the images along with geometrical constraints concerning the road. Some examples are given to demonstrate the efficacy of the method.

Sowers, James P.↗

NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the respective standard approach. Of the methods tested, standard ComBat and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

GeneLab↗

NASA NTRSDate not supplied

Expedited Model Building Method for Rocket Nozzle Side Loading

Statistical analysis of test data is currently the preferred method of creating models for uncharacterized side loading in rocket nozzles. However, statistical analysis requires multiple data sets to be reliable; getting these sets requires building and testing multiple rocket motors, which is not feasible given the incredible time and cost required to do so. By using a combination of geometry, simplified finite element analysis (FEA) analogs, and algebra we can create a limited predictive model from only one test data set. Once created, the model requires only the input from one strain gauge located at each yaw and pitch attachment bracket for the actuation system. This process is not computationally intensive, therefore saving an abundance of time and budget over using full 3D FEA models to develop a similar predictive model. To develop the model, conversion factors between the strain at the gauge locations and corresponding actuator load is derived from the data. These conversion factors are used to create a “conversion ellipse” that bounds the constitutive properties of the nozzle as one lumped factor. Using a 2D cross-sectional analog of the nozzle exit plane, we create a function that allows us to determine the azimuthal location of the unexpected load based on the ratio between yaw and pitch strain readings. Combining this with the “conversion ellipse” yields a function that given the strain reading outputs an induced actuator load, regardless of the azimuthal location of the unexpected load. As previously mentioned, this predictive model is limited in its application as there is inaccuracy introduced by the use of data by only one test as well as simplifying assumptions. This approach makes a useful tool to quickly determine whether uncharacterized actuator loading events can damage actuation systems or induce loading that exceeds design limits.

Seetha A Kolli↗

NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗

NASA NTRS2004

An Analysis Methodology for the Gamma-ray Large Area Space Telescope

The Large Area Telescope (LAT) instrument on the Gamma Ray Large Area Space Telescope (GLAST) has been designed to detect high-energy gamma rays and determine their direction of incidence and energy. We propose a reconstruction algorithm based on recent advances in statistical methodology. This method, alternative to the standard event analysis inherited from high energy collider physics experiments, incorporates more accurately the physical processes occurring in the detector, and makes full use of the statistical information available. It could thus provide a better estimate of the direction and energy of the primary photon.

Morris, Robin D.↗