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NASA NTRS2016

Advances in Rodent Research Missions on the International Space Station

A research platform for rodent experiment on the ISS is a valuable tool for advancing biomedical research in space. Capabilities offered by the Rodent Research project developed at NASA Ames Research Center can support experiments of much longer duration on the ISS than previous experiments performed on the Space Shuttle. NASAs Rodent Research (RR)-1 mission was completed successfully and achieved a number of objectives, including validation of flight hardware, on-orbit operations, and science capabilities as well as support of a CASIS-sponsored experiment (Novartis) on muscle atrophy. Twenty C57BL6J adult female mice were launched on the Space-X (SpX) 4 Dragon vehicle, and thrived for up to 37 days in microgravity. Daily health checks of the mice were performed during the mission via downlinked video; all flight animals were healthy and displayed normal behavior, and higher levels of physical activity compared to ground controls. Behavioral analysis demonstrated that Flight and Ground Control mice exhibited the same range of behaviors, including eating, drinking, exploratory behavior, self- and allo-grooming, and social interactions indicative of healthy animals. The animals were euthanized on-orbit and select tissues were collected from some of the mice on orbit to assess the long-term sample storage capabilities of the ISS. In general, the data obtained from the flight mice were comparable to those from the three groups of control mice (baseline, vivarium and ground controls, which were housed in flight hardware), showing that the ISS has adequate capability to support long-duration rodent experiments. The team recovered 35 tissues from 40 RR-1 frozen carcasses, yielding 3300 aliquots of tissues to distribute to the scientific community in the U.S., including NASAs GeneLab project and scientists via Space Biology's Biospecimen Sharing Program Ames Life Science Data Archive. Tissues also were distributed to Russian research colleagues at the Institute for Biomedical Problems. The expression levels of select genes including albumin, catalase, GAPDH, HMGCoA Reductase, and IGF1 were determined using RNA isolated from the livers by qPCR and no significant differences by one factor ANOVA were found between flight and ground control groups. In addition, some of the liver samples were analyzed for transcriptomic, epigenomic and proteomic profiles; some of the data sets are now available to the scientific community through GeneLabs open science data website. A second long duration mission, Rodent Research-2 (RR-2) was completed on the ISS in 2015; 20 female C57BL6J mice were successfully maintained on the ISS for various durations, with the last group of 5 animals living on-orbit for 54 days. Furthermore, we continue to expand the ISSs capabilities by introducing new on-orbit technologies including blood collection and separation, bone densitometry scanning, muscle grip strength and anesthesia with recovery. In addition, series of ground-based verification testing to fly male mice and increase the total number of mice on-orbit from 20 to 40. Subsequent missions will provide the capability to return live mice from the ISS animals to evaluate recovery on Earth, further expanding operational and science capabilities of the RR project on the ISS.

Choi, S. Y.↗
NASA NTRS2016

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

model structure uncertainty↗
NASA NTRS2018

Effect of Ex Vivo Ionizing Radiation on Static and Fatigue Properties of Mouse Vertebral Bodies

For a variety of medical and scientific reasons, human bones can be exposed to a wide range of ionizing radiation levels. In vivo radiation therapy (0.05 kGy) is used in cancer treatment, and ex vivo irradiation (25-35 kGy) is used to sterilize bone allografts. Ionizing radiation in these applications has been shown to increase risk of fracture, decrease bone quality and degrade collagen integrity. Past studies have investigated the deleterious effects of radiation on cortical or trabecular bone specimens individually, but to date no studies have examined whole bones containing both cortical and trabecular tissue. Furthermore, a clear relationship between the dose and the mechanical and biochemical response of bone's extracellular matrix has yet to be established for doses ranging from cancer therapy to allograft sterilization (0.05-35 kGy). To gain insight into these issues, we conducted an ex vivo radiation study to investigate non-cellular (i.e. matrix) effects of ionizing radiation dose on vertebral whole bone mechanical properties, over a range of radiation doses (0.05-35 kGy), with a focus on any radiation-induced changes in collagen. With underlying mechanisms of action in mind, we hypothesized that any induced reductions in mechanical properties would be associated with changes in collagen integrity. METHODS: 20-week old female mice were euthanized and the lumbar spine was dissected using IACUC approved protocols. The lumbar vertebrae (L1- S1) were extracted from the spine via cuts through adjacent intervertebral discs, and the endplates, posterior processes, surrounding musculature, and soft tissues were removed (approx. 1.5mm diameter, approx. 2mm height). Specimens were randomly assigned to one of five groups for ex vivo radiation exposure: x-ray irradiation at 0.05, 1, 17, or 35 kGy, or a 0 kGy control. Following irradiation, the vertebrae were imaged using microcomputed tomography (micro-CT) and then subjected to either monotonic compressive loading to failure or uniform cyclic compressive loading. During cyclic testing, samples were loaded in force control to a force level that corresponded to a strain of 0.46%, as determined in advance by a linearly elastic micro-CT-based finite element analysis for each specimen. Tests were stopped at imminent fracture, defined as a rapid increase in strain. The main outcome for the monotonic test was the strength (maximum force); for cyclic testing it was the fatigue life (log of the number of cycles of loading at imminent failure). A fluorometric assay was used on the S1 vertebrae to measure the number of non-enzymatic collagen crosslinks[4]. A one-way ANOVA was performed on mechanical properties and collagen crosslinks; means were compared with controls using Dunnett's method, with a Tukey-Kramer post-hoc analysis when significance was found (p < or = 0.05). RESULTS: Compared to the unirradiated control group, the concentration of non-enzymatic collagen crosslinks was significantly increased for all irradiated groups (p < 0.0001), and being higher by at least 50% (Figure 1a). By contrast, the radiation effects on the collagen were only evident at the higher doses. For irradiation exposures of 17 kGy or more, strength decreased substantially as the radiation level was increased, but no effect was evident below 17 kGy (Figure 1b). There was no significant change in the stiffness or maximum displacement for any radiation dose (p>0.05). The finite element analysis prescribed force level for cyclic loading exceeded the measured (monotonic) strength of the 17 and 35 kGy irradiated groups (mean +/- SD, 20.6 +/- 5.6 N; 13.2 +/- 3.7 N, respectively) and therefore these groups were eliminated from the fatigue study. The fatigue life for the 0.05 and 1 kGy groups were similar to each other and were not statistically significantly different from the control group (Figure 1c).

radiation↗
NASA NTRS2016

Effects of Hindlimb Unloading and Ionizing Radiation on Murine Gene Expression in Skin and Bone

Long duration spaceflight causes a negative calcium balance and reduces bone density in astronauts. The underlying mechanisms of spaceflight-induced bone loss and the possible influences of both microgravity and radiation are not fully understood although emerging evidence suggests that these two factors may interact to result in increased bone loss. Previously, gene expression analysis of hair follicles from astronauts, as well as skin from space-flown mice, revealed changes in the expression of genes related to DNA damage and oxidative stress responses. These results resemble the responses of bone to spaceflight-like radiation and simulated weightlessness by hindlimb unloading (HU). Hence in this study, we initiated studies to determine whether skin can be used to predict the responses of bone to simulated microgravity and radiation. We examined oxidative stress and growth arrest pathways in mouse skin and long bones by measuring gene expression levels via quantitative polymerase chain reaction (qPCR). To investigate the effects of irradiation andor HU on gene expression, we used skin and femora (cortical shaft) from the following treatment groups: control (normally loaded, sham-irradiated) (CT), hindlimb unloading (HU), 56Fe radiation (IR) and both HU+IR. Animals were euthanized 11 days post-IR, and results were analyzed by 1-way ANOVA. In skin samples, Cdkn1a was decreased to the same extent in HU and HU+IR (47 of CT). In addition, HU reduced FoxO3 expression (46 of CT) and IR increased Gadd45g expression 135 compared CT in skin. But in bone, HU increased FoxO3 expression 31 compared the level of CT. These results suggest that radiation and simulated weightlessness regulated simliar oxidative stress and cell cycle arrest genes in both skin and bone, although the time course and direction of changes may differ. This research may lead to the development of a relatively simple diagnostic tool for bone loss with the advantage that hair follicles and skin are relatively easy to acquire from subjects.

Terada, Masahiro↗
NASA NTRS2016

Advances in Rodent Research Missions on the International Space Station

A Research platform for rodent experiment on the ISS is an essential tool for advancing biomedical research in space. The Rodent Research allows for experiments of much longer duration that experiments on the Shuttle. NASA’s Rodent Research (RR)-1 mission was successfully completed, including post-flight analysis and achieved a number of objectives including validation of flight hardware, on-orbit operations, and science capabilities that were developed at the NASA Ames Research Center. Briefly, twenty C57BL/6J adult female mice were launched on the SpX4 Dragon vehicle, which thrived for up to 37 days in microgravity. Daily health checks of the mice were performed during the mission via downlinked video; all animals were healthy and displayed normal behavior without any significant signs of stress. Behavioral analysis demonstrated that Flight and Ground Control mice exhibited the same range of behaviors, including eating, drinking, exploratory behavior, self- and allo-grooming, and social interactions indicative of healthy animals. The animals were euthanized and select tissues were collected from some of the mice on orbit to assess the long-term sample storage capabilities of the ISS. The data obtained from the flight mice were comparable to those from the 3 groups of control mice (baseline, vivarium and ground controls), suggesting that the ISS has adequate capability to support long-duration rodent experimentations. We recovered over 35 tissues from 40 RR1 frozen carcasses, yielded over 3200 aliquots of tissues, and distributed to the scientific community, including NASA’s GeneLab and scientists in the U.S. through Biospecimen Sharing Program via Ames Life Science Data Archive. Tissues were also distributed to Russian research colleagues at the Institute for Biomedical Problems. The expression levels of select genes including albumin, catalase, GAPDH, HMGCoA Reductase, and IGF1 were determined using RNA isolated from the livers by qPCR and no significant differences by one factor ANOVA were found between flight and ground control groups. In addition, some of the liver samples were subject to transcriptomics, epigenomics and proteomics. The data are now available to the scientific community through GeneLab’s open science data website. Since the RR1 mission, another long duration mission (Rodent Research-2) was completed on the ISS in 2015 in which 20 female C57 BL/6J mice were successfully maintained on the ISS for varying time points, with the last group of 5 animals being on-orbit for 54 days. This second Rodent Research flight expanded the program’s capabilities with the introduction of new technologies including blood collection and separation and bone densitometry scanning. Furthermore, we have continued to expand the ISS’s capabilities by running a series of ground-based verification testing using male mice. Our next step is to fly male mice for Rodent Research-4 on SpaceX-10 to study the effects of microgravity on bone healing and regeneration. It will be the first long-duration mission using male mice using Rodent Hardware. In addition, the number of mice will increase from 20 mice (on RR-1 and RR-2) to 40 for RR-4. When samples return to Earth, a number of tissues will be dissected from the frozen carcasses and select tissue samples will become available to the scientific community via BSP. Altogether, we have continued to expand our capabilities for performing long-duration missions on the ISS as emphasized in the National Research Council’s Decadal Survey released in 2011 and to maximize science return from each mission.

Choi, S.↗
NASA NTRSDate not supplied

Terrestrial sedimentation impact on coral and macroalgal cover in Puerto Rico's southwest coral reefs.

The coral reefs in southwest Puerto Rico (PR) have faced coral cover reduction in the last decades due to changes in its environment that can be related to sedimentation entrance resulting from anthropogenic activities in the watershed (i.e., agriculture and urban development). The Guánica Bay and La Parguera reef platforms are two natural reserves important for recreational and fishing activities. Nonetheless, their respective coral cover has decreased dramatically. Data from a previous NASA-funded project (HICE-PR) conducted between 2014-2018 was analyzed to better understand how the terrigenous sedimentation can affect the coral reef benthic composition in Guánica Bay and La Parguera. A one-way Nested ANOVA and a Tukey's HSD analyses were applied to find differences in benthic cover composition within and among the sites and location. The preliminary results showed differences in coral cover and macroalgae cover between the platforms reefs sites. The coral cover in La Parguera was higher and averaged 11.5% compared to that of Guánica (8.6%), while the macroalgae cover followed a similar pattern. Both coral cover and macroalgae in La Parguera and Guánica varied between from 2016 to 2018 with a general trend of increased coral cover in La Parguera and increase in macroalgae in Guánica. These trends can be related to the distance to the coastline as those reefs located closer to the coastline showed a greater percentage in macroalgal cover than those located farther. Cover of macroalgae and corals at reefs located a mid-distance to the coastline varied with no definite pattern detected. These results will be further correlated with variations in water quality obtained from remotely-sensed data (patterns of chlorophyll a, total suspended sediments, vertical attenuation coefficient) to support the prediction and tracking of coral cover changes in PR's southwest region.

macroalgal↗
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 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

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↗
NASA NTRSDate not supplied

Parameters Impacting Columnated Granular Soil Pneumatic Seal Performance

The ability of a column of loose granular soil to form a pneumatic seal was investigated by varying the diameter of the soil column, the effective column height, and the level of compaction in the soil. The soil column diameter was tested at three levels using pipes with inner diameters measuring 5.08, 10.16, and 15.24 centimeters (2, 4, and 6 inches). Soil was filled in each pipe to form 15, 30, and 45 centimeter (6, 12, and 18 inch) tall soil columns. Each diameter/height configuration was also tested at three levels of soil compaction, compared by calculating the bulk density of the soil with mass and volume measurements. GRC-1a simulant was used, with approximate low/medium/high bulk densities of 1.6, 1.75, and 1.9 g/cc achieved with a combination of vibration and tamping. The order of tests for a given column diameter was randomized and repeated three times. With the top of the soil open to atmosphere at room temperature, compressed air was injected through a small diffuser at the column base with several small downward-facing holes. The number and size of these holes was scaled such that a constant total inlet orifice area to column cross sectional area ratio was maintained for each column diameter. Inlet air pressure was slowly increased via a precision regulator to preserve quasi-static equilibrium in the soil column to minimize the impact of dynamics. Air pressure was increased all the way through the static and bubbling regimes until slugging or turbulent behavior was observed in the soil to ensure that the entire static regime had been captured during data collection. A three-factor, three-level analysis of variance (ANOVA) statistical analysis was performed on the resulting data to determine the extent to which each physical parameter impacted the soil column seal performance. It was concluded that there is statistically significant evidence that column height, and the interaction between column height and diameter impact soil seal performance. In all other cases there was insufficient data to identify a statistically significant causal relationship. Additionally, plots were generated comparing experimental data to the predictive formula developed by Ogino et al. for fluidized beds in 1993. Because this model was developed for industrial spouted fluidized beds, the accuracy of its output prior to fluidization in the static seal ‘edge case’ is unknown, especially considering in this application the working gas was diffused across the column base rather than being injected through a spout. Further, the fidelity of the model had not yet been tested with lunar soil simulants. Plotting the Ogino et al. model alongside test data allows for a more intuitive sense of the impact of test parameters on soil seal performance, as well as providing a quick means to further tune this predictive model for more accurate use with static seals across granular lunar soil simulants. The model provided by Ogino et al. was further tuned using test data to determine the degree to which a spouted bed model could be applied to a slightly modified set of testing conditions: lunar soil simulants and a more diffuse, homogeneous application of pneumatic pressure. A Matlab script was created to test different values for the leading coefficient and exponents in Ogino’s formula. The script swept preset ranges, then iterated with higher resolutions across narrower ranges to converge on the optimal value for each parameter. The resulting adjusted model was compared to the original, as well as test data with noticeable improvements across the entire test domain.

Jack Stewart↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRSDate not supplied

Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets

RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

Lauren M. Sanders↗
NASA NTRSDate not supplied

Efficacy of Dried Plum to Countermeasure Simulated Spaceflight Exposure

Human exploration of the space environment has been a topic of interest for decades; however, the threat to physiological health posed by ionizing radiation and microgravity effects remains a concern. Previous research utilizing combined hindlimb unloading and irradiation to simulate spaceflight effects found that a dried plum diet can mitigate adverse effects of simulated spaceflight on skeletal health. Though the mechanism is not fully understood we believe this protective effect is due to its high antioxidant capacity and polyphenolic content. This study seeks to extend these findings to investigate if dried plum is an effective countermeasure in other tissues relating to aging and neurotoxicity. Male C57BL/6 J mice were assigned to one of eight groups (n = 10/group) and were fed either a controlled diet or dried plum diet, underwent normal loading or hindlimb unloading as well as exposure 2 Gy gamma radiation or sham exposure. At the end of the experiment, mice were euthanized, and RNA was extracted from brain and bone marrow tissue and PCR was employed to analyze significant changes in gene expression profiles. Analysis of five house-keeping genes revealed genes B2M (neurotoxicity) and GUSB (aging) to be the most stable genes across all treatments and were used to find the fold change within our genes of interest. Through this, and analysis via ANOVA, we were able to identify genes with significant altered expression levels within brain and bone marrow tissues related to neurotoxicity and aging. Considering previous research, we hypothesize that the dried plum diet’s antioxidative properties ameliorated the oxidative stress from irradiation and microgravity via altered levels of gene expression. Identified expression changes will provide insights into molecular mechanisms behind the observed protective effects. These findings present potential avenues for future interventions to alleviate negative impacts of spaceflight on astronaut health.

Faisal Ahmad↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRSDate not supplied

Effects of Mild Hypobaric Hypoxia on Visual Field Impairment

INTRODUCTION: The primary objective of the Exploration Atmosphere (EA) study is to validate a new prebreathe protocol necessary before exposure to a suited hypobaric environment during extravehicular activity (EVA) from a habitat ‘exploration atmosphere’ of 56.5kPa (8.2 psia), 34% O2, 66% N2. Prebreathe protocols must be time and resource efficient while also controlling Decompression Sickness (DCS) risk to within acceptable limits. The habitat hypobaric exploration atmosphere also results in a mildly hypoxic environment (piO2 = 128mmHg). As a secondary objective of the EA study, we characterized the effects of 11-day exposure to a mild hypobaric hypoxic environment on visual performance. METHODS: Two, 11-day hypobaric chamber tests were performed (EA-1and EA-2, n=8 each) in NASA’s 20-foot chamber at Johnson Space Center where subjects lived in the exploration atmosphere. Subjects also underwent simulated 6-hour EVAs at 85% O2 and 29.6 kPa during EVA on days 3, 5, 7, 9, and 11. Visual acuity (VA), a measure of spatial resolution, and contrast sensitivity (CS), a measure of ability to distinguish ever finer increments of brightness, were assessed on non-EVA days by using gapped Landolt C testing. The luminance was controlled by using a booth, a light monitor, and a lighting rheostat. EA-1 data had revealed problems in lighting control impacting consistency of the data. Procedures were subsequently updated for the EA-2 test. RESULTS: EA-1 data revealed large variance and data recording errors and was removed from the analysis. One participant left the study at Test Day 3 during EA-2. EA-2 ANOVA results showed CS (mean change=0.010 logCSWeber) and VA (mean change, -0.016 logMAR) between pre-test and 11-day test phases; however, neither VA nor CS changes were statistically significant. There were non-statistically significant declines in VA across test phases. DISCUSSION: Overall, visual field performance did not exhibit clinically significant changes (3 lines or greater change in LogMar chart) during exposure to the mild hypoxic exploration atmosphere environment compared to pre-test baseline. The consistency and stability of VA data during EA-2 suggests that the mild hypobaric hypoxic environment did not cause clinically significant negative impacts to participants’ visual field performance.

Visual Acuity↗
NASA NTRSDate not supplied

The Efficacy of Dried Plum to Counter Spaceflight Microgravity and Radiation

Human exploration of the space environment has been a topic of interest for decades; however, the threat to physiological health posed by ionizing radiation and microgravity effects remains a concern. Previous research utilizing combined hindlimb unloading and irradiation to simulate spaceflight effects found that a dried plum diet can mitigate adverse effects of simulated spaceflight on skeletal health. Though the mechanism is not fully understood we believe this protective effect is due to its high antioxidant capacity and polyphenolic content. This study seeks to extend these findings to investigate if dried plum is an effective countermeasure in other tissues relating to aging and neurotoxicity. Male C57BL/6 J mice were assigned to one of eight groups (n = 10/group) and were fed either a controlled diet or dried plum diet, underwent normal loading or hindlimb unloading as well as exposure 2 Gy gamma radiation or sham exposure. At the end of the experiment, mice were euthanized, and RNA was extracted from brain and bone marrow tissue and PCR was employed to analyze significant changes in gene expression profiles. Analysis of five house-keeping genes revealed genes B2M (neurotoxicity) and GUSB (aging) to be the most stable genes across all treatments and were used to find the fold change within our genes of interest. Through this, and analysis via ANOVA, we were able to identify genes with significant altered expression levels within brain and bone marrow tissues related to neurotoxicity and aging. Considering previous research, we hypothesize that the dried plum diet’s antioxidative properties ameliorated the oxidative stress from irradiation and microgravity via altered levels of gene expression. Identified expression changes will provide insights into molecular mechanisms behind the observed protective effects. These findings present potential avenues for future interventions to alleviate negative impacts of spaceflight on astronaut health.

Faisal Ahmad↗
NASA NTRS2024

Findings From 9 Years of Renal Stone Surveillance of U.S. Astronauts

INTRODUCTION: Renal stones are thought to be a risk for spaceflight due to urine chemistry changes, induced by microgravity. Asymptomatic stone can have significant medical and mission impact.For9 years astronauts travelling to the International Space Station (ISS) have received pre-and post-flight renal ultrasound, according to a specialized renal ultrasound protocol, to look for renal calcifications or mineralized renal material (MRM).A new ultrasound machine was put in service halfway through the screening program. METHODS: MRM screening data from pre-and post-flight were compared, to look for changes in MRM burden. Pre-and post-flight scans from 42 subjects were compared. Studies were further stratified by old versus new machine. Measures were modeled using the analysis of variance (ANOVA) as defined by the interaction between pre-to postflight, and machine-to-machine. RESULTS: There was no observed statistical change seen in MRM burden pre-to postflight using the new higher-resolution ultrasound machine. DISCUSSION: The new machine gives more precise measurements and allows us to differentiate MRMs from other bright objects seen on ultrasound, such as small blood vessels. These data imply that spaceflight does not increase the risk of renal stone formation, at least out to the length of ISS missions. This data will be used to update NASA’s Human System Risk Board’s risk tracking documents, in-flight Concept of Operations, and our Clinical Practice Guideline.

K Cortez↗
NASA NTRSDate not supplied

Changes in Oculomotor Behavior and PVT Reaction Time During One Night of Sleep Deprivation

INTRODUCTION: The Psychomotor Vigilance Test (PVT) is a widely used objective measure of sustained attention, alertness, and fatigue. Previous studies consistently show that prolonged wakefulness and disrupted sleep patterns lead to increased lapses of attention and slower reaction times. These findings have critical implications for various professions, including healthcare, aviation, transportation safety, and spaceflight. In this study, we employed linear mixed models (LMM) to investigate hourly-measured 5-minute PVT reaction times during one night of total sleep deprivation. Our goal was to characterize the contribution of homeostatic sleep pressure (time awake) and circadian phase (salivary melatonin levels) on the dynamics of PVT reaction time. MATERIALS & METHODS: Data from twelve human participants were used in the analysis. Participants were healthy non-smokers, aged 18 and 40, with normal sleep habits defined as Pittsburg Sleep Quality Index scores < 5, and Morningness – Eveningness Questionnaire scores > 42 and < 58, respectively. Participants followed a constant-routine protocol and arrived at the NASA sleep laboratory approximately 1 to 2 hours after their habitual waking time. Tympanic temperature was measured every 30 min to provide a real-time estimate of circadian phase. Participants were required to stay awake throughout the laboratory experiment, which continued until their tympanic temperature had returned to baseline levels. This approach ensured that we could capture each participant's circadian trough and recovery, typically occurring between 24 and 26 hours after waking. The PVT and saliva melatonin were measured every hour, starting approximately from 3 hours after awakening. More information about the measurement protocol can be found in the publication by Stone et al. We used PVT reaction times between 100 and 10000 ms to calculate Reciprocal Reaction Times (RRTs). LMM models were employed to assess the contribution of time awake and saliva melatonin levels on changes in RRTs. Two models were created: the first model treated time awake as a fixed effect and the second model considered both the salivary melatonin level and time awake as fixed effects. Participants were included as a random effect in both models. We compared the models using an Analysis of Variance (ANOVA). The models were computed using the 'lmer' function from the 'lme4' package in R, and LMM p-values were determined using Satterthwaite's method. RESULTS: We found that time awake had a statistically significant effect on PVT RRTs during one night of sleep deprivation [χ2 = 2132.4, df = 1, p < 0.001]. In addition, the model including both saliva melatonin levels and time awake was statistically significantly better than one that uses just time awake [χ2 = 17.4, df = 1, p < 0.001]. DISCUSSION & CONCLUSION: As expected, these preliminary results suggest that the time awake and circadian phase each contribute to performance reductions as measured by the PVT. Future analyses will explore the contributions of other factors to the model.

sleep deprivation↗