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Diana Gentry

Publications and source records attributed to Diana Gentry.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Future of the Search for Life: Workshop Report

The 2-week, virtual Future of the Search for Life science and engineering workshop brought together more than 100 scientists, engineers, and technologists in March and April 2022 to provide their expert opinion on the interconnections between life-detection science and technology. Participants identified the advances in measurement and sampling technologies they believed to be necessary to perform in situ searches for life elsewhere in our Solar System, 20 years or more in the future. Among suggested measurements for these searches, those pertaining to three potential indicators of life termed “dynamic disequilibrium,” “catalysis,” and “informational polymers” were identified as particularly promising avenues for further exploration. For these three indicators, small breakout groups of participants identified measurement needs and knowledge gaps, along with corresponding constraints on sample handling (acquisition and processing) approaches for a variety of environments on Enceladus, Europa, Mars, and Titan. Despite the diversity of these environments, sample processing approaches all tend to be more complex than those that have been implemented on missions or envisioned for mission concepts to date. The approaches considered by workshop breakout groups progress from nondestructive to destructive measurement techniques, and most involve the need for fluid (especially liquid) sample processing. Sample processing needs were identified as technology gaps. These gaps include technology and associated sampling strategies that allow the preservation of the thermal, mechanical, and chemical integrity of the samples upon acquisition; and to optimize the sample information obtained by operating suites of instruments on common samples. Crucially, the interplay between science-driven life-detection strategies and their technological implementation highlights the need for an unprecedented level of payload integration and extensive collaboration between scientists and engineers, starting from concept formulation through mission deployment of life-detection instruments and sample processing systems.

Marc Neveu↗
NASA NTRS2021

Space Biology Research and Biosensor Technologies: Past, Present, and Future

In light of future missions beyond low Earth orbit (LEO) and the potential establishment of bases on the Moon and Mars, the effects of the deep space environment on biology need to be examined in order to develop protective countermeasures. Although many biological experiments have been performed in space since the 1960s, most have occurred in LEO and for only short periods of time. These LEO missions have studied many biological phenomena in a variety of model organisms, and have utilized a broad range of technologies. However, given the constraints of the deep space environment, upcoming deep space biological missions will be largely limited to microbial organisms and plant seeds using miniaturized technologies. Small satellites such as CubeSats are capable of querying relevant space environments using novel, miniaturized instruments and biosensors. CubeSats also provide a low-cost alternative to larger, more complex missions, and require minimal crew support, if any. Several have been deployed in LEO, but the next iterations of biological CubeSats will travel beyond LEO. They will utilize biosensors that can better elucidate the effects of the space environment on biology, allowing humanity to return safely to deep space, venturing farther than ever before.

space biology↗
NASA NTRSDate not supplied

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗
NASA NTRSDate not supplied

BioLEAD: BioLogical Exploration via Autonomous Detection – Using Dielectric Spectroscopy to Monitor Biological Changes in Response to Deep Space Radiation

Leveraging the bio-fluidic hardware developed for BioSentinel, we propose a new payload for autonomous missions: BioLEAD – BioLogical Exploration via Autonomous Detection. With NASA's renewed focus to continue human exploration of the Moon (Artemis Program), the BioLEAD payload aims to investigate the effects of the lunar environment on biology, whether onboard a lunar lander, the Lunar Gateway, or as a free-flying CubeSat orbiting the Moon. In place of an optical detection system, BioLEAD will employ a miniaturized, non-invasive dielectric spectroscopy sensor to enable real-time monitoring of biological activity. The sensor operates by relating capacitance measurements to the dielectric properties of the cell, such as cell morphology, doubling time, and cell cycle stage. The implementation of this new sensor technology will address limitations of the optical measurement system used on BioSentinel. It will also advance the use of autonomous bioanalytical microsystems and reveal new information regarding biological responses to the Moon’s radiation environment. Most importantly, BioLEAD’s technology will be adaptable for a wide array of future missions.

Biosensor↗
NASA NTRSDate not supplied

3D Construction of Biologically Derived Materials

System for the 3D Construction of Biologically Derived Materials, Structures, and Parts NASA has developed a novel approach for macroscale biomaterial production by combining synthetic biology with 3D printing. Cells are biologically engineered to deposit desired materials, such as proteins or metals, derived from locally available resources. The bioengineered cells build different materials in a specified 3D pattern to produce novel microstructures with precise molecular composition, thickness, print pattern, and shape. Scaffolds and reagents can be used for further control over material product. This innovation provides modern design and fabrication techniques for custom-designed organic or organic-inorganic composite biomaterials produced from limited resources. Benefits Conserves resources. Few raw or bulk starting materials needed Enables custom design of diverse materials Fast, portable, macroscale, on-demand manufacturing High-fidelity microstructures Uses commercially available parts Applications Biomaterials, biotechnology Organic-inorganic composite materials On-demand manufacturing In situ resource utilization Space stations Military Infrastructure materials The Technology Once genes for a desired material type, delivery mode, control method and affinity have been chosen, assembling the genetic components and creating the cell lines can be done with well-established synthetic biology techniques. A 3D microdeposition system is used to make a 3D array of these cells in a precise, microstructure pattern and shape. The engineered cells are suspended in a printable 'ink'. The 3D microdeposition system deposits minute droplets of the cells onto a substrates surface in a designed print pattern. Additional printer passes thicken the material. The cell array is fed nutrients and reagents to activate the engineered genes within the cells to create and deposit the desired molecules. These molecules form the designed new material. If desired, the cells may be removed by flushing. The end product is thus a 3D composite microstructure comprising the novel material. This innovation provides a fast, controlled production of natural, synthetic, and novel biomaterials with minimum resource overhead and reduced pre- and post-processing requirements.

3D↗
NASA NTRSDate not supplied

Classifying Agnostic Biosignatures using Raman, VNIR, and Elemental Data

How can we use our current wealth of terrestrial data, encompassing biogenic and abiogenic systems, to determine the distinguishing properties of life? SCOBI (Statistical Classification of Biosignature Information) uses machine learning techniques to algorithmically identify combinations of measurements that are “indicative of life”. A set of ~1000 observations, comprising elemental abundance, isotopic fractionation, VNIR reflectance, and (in progress) Raman spectra, have been assembled from existing literature and databases. The observations cover systems classified as “indicative alive” (e.g., cells, vegetation), “indicative non-alive” (e.g., fossils, teeth), “mixed indicative” (e.g., soil, pond water), or “non-indicative” (e.g., rocks, meteorites). VNIR data was preprocessed by linear interpolation from 400-2100 nm and smoothed with a Savitzky-Golay filter. To limit the amount of Earth-biochemistry-specific (non-agnostic) information included, the first five spectral features extracted were number of peaks, number of troughs, mean reflectance, mean peak width, and broadest peak width. To help further emphasize agnostic biosignatures, Earth-specific features such as chlorophylls have been manually flagged so that feature importance with and without them can be compared. Classifiers including k-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented, as was a combination voting classifier. Performance metrics included false positive rates, false negative rates, and AUC with 50-50 test/train splits (Monte Carlo simulations). Key takeaways from this stage, prior to the inclusion of Raman spectra, are (1) the overall success rate of 0.933 AUC was most heavily influenced by the elemental abundance data; and (2) VNIR reflectance had the lowest classification performance with 0.52 AUC (58% of objects correctly classified). The next steps are to complete integration of Raman spectral data and to improve the approach to pre-processing and feature extraction for both types of spectral data, such as automated baseline removal, whole spectrum matching, and dimensionality reduction.

Biosignatures↗
NASA NTRSDate not supplied

Constraining Venus Cloud Habitability: Earth's Aerobiosphere as an Analogue Environment

The potential habitability of Venus's cloud and haze layers has been debated for decades, with interest fueled by observations including disequilibria in atmospheric chemistry, strongly UV-absorbing particles, and (most recently) a controversial phosphine detection. Venus cloud temperature and pressure are clement by Earth standards; however, the calculated high acid activity and low water activity may exceed the limits of Earth biochemistry, based on observed life in extreme environments. A significant challenge in understanding Venus's habitability is the lack of an appropriate terrestrial analogue environment. Life has not been found in hydrothermal systems approaching Venus aerosol acidities, but these systems also contain high levels of other harsh solutes. Earth's stratospheric sulfate layer is a partial match in terms of acidity, desiccation, size, and isolation from the surface, but the few samples returned from these altitudes have yielded only sparse, inactive cells. Earth's tropospheric cloud droplets have an active microbial presence, but are larger and far more water-rich than Venus aerosols; tropospheric times aloft are also similar to microbial generation times, making Earth's aerobiosphere dependent on continuous surface replenishment. Direct detection of potential biosignatures from a single transect is challenging; Earth's cloud microbiota yields 102 - 108 viable cells per mL, equivalent to one per 103 - 109 particles, with highly dynamic and heterogeneous distribution. The most important in situ measurement for Venus cloud habitability is detailed aerosol composition: confirmation of acid and water activity, presence of potentially bioavailable nutrients and energy, and potential presence of organic matter. Modeling Venus cloud aerosol residence time in comparison to periodic influxes of water and photochemical energy, and thus the ability of an ecosystem to maintain itself over time, would also address a significant habitability constraint particular to aerobiospheres.

Venus↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗
NASA NTRSDate not supplied

Remodeling BioSentinel’s Microbial Metabolism Measurement System

BioSentinel, the first biological CubeSat beyond low Earth orbit, will investigate how Saccharomyces cerevisiae responds to ionizing radiation using optical measurements of a colorimetric metabolic indicator dye (alamarBlue). To map optical data to more detailed metabolic measurements, a microbial culture system for ground testing was designed consisting of a 400 mL beaker and a rubber stopper with pass-through holes for its five sensor probes (electrical conductivity, pH, oxidation-reduction potential, dissolved oxygen, optical density). The initial design had several drawbacks. It was only partially reusable due to its one-time sealants: Teflon tape and parafilm. A steady increase in oxygen levels within the sensor’s testingdata indicated the design was not sufficiently airtight. Degradation of the rubber stopper’s elasticity due to sterilization and reinsertion of the probes led to poor robustness and eventual replacement. The vessel’s large headspace didn’t reproduce flight conditions and a large culture volume required more reagents. The system has been redesigned to contain a rigid, biocompatible polycarbonate disc lid that allows for threaded inserts. The culture vessel’s height will be reduced from 110 mm to 65 mm to decrease overhead space and material costs. A more reliable seal will be achieved with a gasket and clamps. The optical probe records absorbance at four wavelengths to characterize cell density and alamarBlue dye ratios. A pneumatically operated plunger draws media through the hollow interior of the probe. Currently, deformation of the silicone on the plunger causes asymmetrical movement and a drift from its initial state. Designs are being developed to limit the plunger’s travel for a more accurate measurement cycle. The remodeled system is under construction and will be tested for leaks and accuracy. The measurements collected will be used to provide additional insight into the optical data returned from the BioSentinel mission.

Astrobiology↗
NASA NTRSDate not supplied

Growth Curve Parameterization of Metabolic Activity of Yeast Cells for BioSentinel

The goal of the BioSentinel small satellite payload is to measure the effect of deep space radiation on the growth and metabolic activity of yeast cells. Raw test data is generated by fluidics cards containing yeast cells rehydrated at different periods, with metabolic activity measured by the reduction of alamarBlue. Each card well has a sensor array that measures the amount of red, green, and infrared light transmitted through the yeast culture. This illumination data is then converted to absorbance values, which are further converted into concentrations. The ultimate objective is to convert these concentrations into biologically-relevant metrics that can be compared against one another to determine changes due to differential radiation exposure. Beginning with IR absorbance data (corresponding to cell density) from ground studies, three parameters from a sigmoidal growth curve were extracted and analyzed: 𝜆 (lag phase), 𝜇 (max growth rate), and A (max cell growth). The data was fit to the Gompertz model of microbial growth using non-linear regression (Minitab), as the fit error was reduced compared to the simpler logistic growth curve. Graphs showed that the data contained a discrepancy (drift) in the lag phase that is attributable to a slow, constant loss of moisture. Correcting this discrepancy by fitting the first 25 hours of the data to a power function and subtracting these values from the absorbance readings obtained a better statistical fit to the growth curve in the lag phase. A power fit was selected over a linear fit because it reflected the effects of constant volume loss. This correction to the BioSentinel data analysis pipeline will enable quantitative statistical analysis of the effect of different levels of deep space radiation on yeast cells. Future work includes automation of drift correction and curve modeling to extract these parameters directly from data.

Growth Curve↗
NASA NTRSDate not supplied

Tracking Metabolic Changes in Microbial Culture using Redox Measurements

During long-term space missions, microbial cultures accumulate the effects of low-dose radiation, microgravity, and other factors; altered growth and metabolic activity may occur before viability effects. This could affect functionality of bioreactors or other bio-enabled mission systems, as well as shed light on human health. Spaceflight microbiology studies beyond the low Earth orbit exposure afforded by the ISS have been limited. Nanosatellites offer an increasingly popular alternative for deep space missions. However, the communications delay requires biofluidic automation of a pre-defined experimental protocol, and the lack of sample return (reliance on sensors in flight) can significantly limit feasible investigations. Previous biological CubeSats (PharmaSat, O/OREOS, EcAMSat) have used alamarBlue, an off-the-shelf formulation of the redox indicator dye resazurin, to track metabolic activity, as will BioSentinel, the upcoming interplanetary microbiology experiment. A series of ground experiments (see abstracts by Liddell, Santa Maria, and A. Kim) were conducted using a microbial culture system outfitted with an electrochemical sensor array (electrical conductivity, pH, oxidation-reduction potential, and dissolved oxygen) with alamarBlue and the same strain of Saccharomyces cerevisiae as BioSentinel. By improving mapping of measured changes in alamarBlue kinetics to physicochemical changes, and ultimately to biological alterations such as shifted metabolic pathways, this work supplements data analyses from past missions and planning for future missions using alamarBlue to characterize space radiation effects. Initial results indicate that alamarBlue acts like a redox buffer; its presence significantly changes redox kinetics in otherwise identical cultures. The initial color change (blue resazurin reduced to red/pink resorufin) appears as a redox plateau. A second plateau, likely corresponding to the second color transition (resorufin to the colorless hydroresorufin), occurs at a lower redox value. The relationship to carbon source exhaustion, dissolved oxygen depletion, cell death, and measured redox potential is complex and still under study.

Tracking↗
NASA NTRSDate not supplied

BioSentinel: NASA’s First Deep Space Biological Mission

Since Apollo 17 in 1972, NASA has sent no humans or other biological organisms outside of Earth’s protective magnetosphere. NASA’s current Artemis program plans to put astronauts back on the Moon and eventually land human missions on Mars. One of the major challenges to long-duration crewed travel and habitation in deep space is an in-depth understanding of the biological effects of space radiation, often convoluted by the impact of reduced gravity. Such missions will require significant countermeasures, likely both technological and biomedical, to protect organisms from chronic radiation exposure. Small satellite missions like CubeSats can inform these countermeasures by investigating model organisms in relevant space environments. The BioSentinel mission is comprised of four segments developed at NASA Ames Research Center: a 6U CubeSat (1U = 10-cm cube), an ISS payload launched in December 2021 and two ground units, one for the mission’s CubeSat and one for the ISS payload. The last three segments have been operational since January 2022 and serve as experimental controls. BioSentinel’s 6U CubeSat is planned to launch as a secondary payload on the Artemis-1 rocket. It will be deployed on a lunar fly-by trajectory and into a heliocentric orbit. BioSentinel will be the first interplanetary satellite to study the biological response to space radiation outside Low Earth Orbit (LEO) in almost 50 years. BioSentinel is a complete, autonomous spacecraft capable of conducting experiments in deep space. Its 4U BioSensor payload is a fully automated and adaptable platform that can perform biological measurements with a range of microorganisms in multiple space environments, including the ISS, free flyers, and other platforms like the Lunar Gateway and lander vehicles. Once it reaches its orbit, BioSentinel’s CubeSat will measure the DNA damage response to ambient radiation in a model organism, the budding yeast Saccharomyces cerevisiae, which will be compared to information provided by an onboard radiation sensor and to data obtained in LEO (on ISS) and on Earth. Once in interplanetary space, fluidic cards containing desiccated yeast will be activated by growth medium addition at different time points throughout the mission. Growth and metabolic activity will be tracked continuously via optical measurements. This paper describes BioSentinel’s objectives, science, data management, and preliminary results from the ISS segment.

BioSentinel↗
NASA NTRSDate not supplied

Analog Environments for Venus Aerosol Instrument and Mission Concept Testing

The most common hypothesis discussed for life on Venus is a habitat in the dense, persistent clouds, which are primarily sulfuric acid with a secondary water component. Although Earth lacks a complete Venus analog environment, several partial analogs exist which can inform our understanding of the requirements for cloud habitability as well as serving as potential testbeds for future Venus instruments or missions. Earth’s tropospheric clouds have larger particles, more water, and shorter lifetimes than Venus’s; however, they have a partial overlap in pressure and temperature, and are our only example of an airborne habitat.They can inform how particle residence time and microbial generation time constrain habitable atmospheric regions. On the biological side, this includes available nutrients and energy, environmental stressors such as radiation, osmotic potential and acidity, and time spent in protective but inactive forms (endospores, cysts, akinetes); on the physical side, gravity, air density and viscosity, thermal lofting, gravity waves, scavenging due to precipitation, and other airflow dynamics, as well as effective particle radius and electrical charge. They can also enable sampling strategy tests for airborne life detection, ranging from10 2 to10 8 viable cells/mL (10 -3 to 10 -9 cells/particle). Conversely, Earth's stratospheric sulfate layer lacks an active microbial presence, and is at a colder and less dense altitude but has partially analogous values in terms of H2SO4 and water concentration, particle size, and number density. These aerosols are accessible to aircraft and high-altitude balloons. Even more accessible potential analogs include sea spray, which overlaps with the smaller (haze) particle sizes and densities at Venus and contains trace organic constituents; marine fog, which at higher densities can match Venus’s larger (Mode 2) particle properties and contains a range of trace compounds including ammonia, phosphates, sulfates, and nitrates; and spray from acidic hydrothermal systems, which can cover a wide variety of sizes, densities, and chemical compositions. These analogs could improve the development of Venus cloud sampling instruments or sondes, including field tests of material compatibility, particle capture efficiency, and analysis sensitivity.

Analog↗
NASA NTRSDate not supplied

Microbial Optical Data Processing: A Key Step in the Metabolic Assessment of Lunar Explorer Instrument for Space Biology Applications (LEIA) and Biosentinel’s Payload Data

The BioSensor payload platform on BioSentinel and LEIA autonomously collects optical data from microbial model organisms in liquid culture. The BioSensor is designed to monitor metabolic activity using absorbance measurements of cell density and alamarBlue, a readily available colorimetric redox indicator dye. BioSentinel, a pioneering NASA CubeSat, uses yeast to study deep space radiation. LEIA investigates radiation and lunar gravity response. The experimental setup includes 16 wells equipped with three LEDs (570, 630, and 850 nm) and their corresponding photodetectors. One well is a calibration control without biology while the rest have desiccated cultures. Autonomous rehydration initiates the experiment. Data from the BioSensor are received from the flight and ground units, enabling comparison to uncover location-based metabolic rate variations. This study presents a Python Jupyter notebook developed for efficient data processing of multiple CSV files containing date and time columns, temperature, and well illumination data. It offers a user-friendly interface while maintaining computational power, automatically recognizing and iteratively processing data files in a user-input path. A Hampel filter with a short window eliminates outlier artifacts from sensor dropout. Because absorbance is a relative measurement, conversion from raw illumination requires defining a “blank” value, so the first data points are averaged to provide the necessary denominator. A cube-root function correction mitigates undesired drift caused by air pockets during the fluidic card filling phase, maintaining optical path length consistency. Beer-Lambert's law is applied to further convert absorbance values to cell and dye form concentrations, the desired science parameters. The processed data are saved and visualized as SVG plots. Future plans include extracting specific science parameters from the processed data like growth rate and metabolic rate, and identification of features corresponding to metabolic and phenotypic shifts such as starvation, shifts from aerobic to anaerobic growth, and osmotic stresses.

Space biology↗
NASA NTRSDate not supplied

Microbial Vessel for Impedance Spectroscopy and Electrochemistry (Mvise): an Extensible, Interoperable Data Acquisition Platform for Liquid Culture Studies in Space Biology Research

The White House Office of Science and Technology Policy (OSTP) has declared 2023 to be the Year of Open Science following an initiative to democratize scientific knowledge. Simultaneously, new sensor technologies have broadened the experimental space available to bioastronautics research. With these open-science goals and technological advances in mind, we have designed and constructed a data acquisition platform for high-precision, real-time monitoring of liquid culture systems. The vessel rig is fitted with six Atlas Scientific probes (micro pH, electrical conductivity, dissolved oxygen, oxidation-reduction potential, liquid temperature, air CO2) and a custom optical density probe similar to the one on BioSentinel’s BioSensor payload. A custom dielectric spectroscopy probe is also planned. The structure of the vessel is resin 3-D printed on a hobbyist-level machine, reducing the production cost and iteration time by over 60% each while increasing extensibility. Data acquisition and storage is controlled with a standalone C state machine-based program running on a Raspberry Pi 3 Model B. When not running headless, an additional program automatically generates and updates plots for live data visualization. Validation of the rig as a data collection system was performed with a yeast liquid culture experiment. While the vessel rig is currently used for standalone experiments, it can also be used as the base perception unit in a self-driving laboratory (SDL). SDLs are high-throughput data collection systems that employ automation and artificial intelligence to conduct and manage routine experiments. Here, we envision an SDL driven by several vessel rigs in which an automated script compares key results, informing the design of future experiments. A vessel rig SDL would streamline many operations, including 1) strain selection for the Lunar Explorer Instrument for space biology Applications (LEIA) investigation and 2) the study of bioregenerative life support systems (BLSS). Ultimately, the datasets that can now be acquired will provide crucial information for accelerating bioastronautics application development in the era of commercial space.

Stephen Lantin↗
NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗
NASA NTRSDate not supplied

AMMPER: a user-friendly agent-based model that recapitulates simple metabolic responses of yeast to deep-space radiation

For humans venturing to deep space, radiation exposure poses a major health risk. Fundamental research into the biological effects of space radiation are essential for enabling exploration, and the first experimental organisms we send to deep space will be microbial. Yet there are many ways in which microorganisms are likely to experience the effects of high-energy particle radiation (such as Galactic Cosmic Rays) differently from multicellular animals, partly due to the simple fact that microbes are small and unicellular-- less likely to get hit in the first place, and less likely to communicate damage between cells. Computational modeling can aid in designing experiments and predicting the biological effects of radiation, but thus far particle radiation models have not focused on microbes. Here we present the latest developments in AMMPER, the Agent-based Model for Microbial Populations Exposed to Radiation. Originally written in 2021, AMMPER is a Python-based model that incorporates radiation track data from NASA's RITRACKS software and simulates the growth, damage, and death of yeast cells in 3D. It is now freely available as an open-source package on NASA's GitHub repository. Recent improvements include the ability to simulate the dynamics of alamarBlue, a color-changing redox dye commonly used to track metabolic activity in microbial spaceflight experiments. We demonstrate that a simple blue-pink-clear transition model is able to recapitulate key features observed in empirical data from ground studies. AMMPER also includes a new graphical user interface and introductory tutorial to facilitate ease of use by a wider audience. AMMPER can help us to understand how spatially heterogeneous particle radiation damage at the single-cell level can translate to growth differences at the population level, ultimately allowing us to better interpret experiments using microbes as model organisms and how well their results apply to humans.

yeast↗