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41 records · Page 3

Space Station Biological Research Project Habitat: Incubator

Developed as part of the suite of Space Station Biological Research Project (SSBRP) hardware to support research aboard the International Space Station (ISS), the Incubator is a temperature-controlled chamber, for conducting life science research with small animal, plant and microbial specimens. The Incubator is designed for use only on the ISS and is transported to/from the ISS, unpowered and without specimens, in the Multi-Purpose Logistics Module (MPLM) of the Shuttle. The Incubator interfaces with the three SSBRP Host Systems; the Habitat Holding Racks (HHR), the Life Sciences Glovebox (LSG) and the 2.5 m Centrifuge Rotor (CR), providing investigators with the ability to conduct research in microgravity and at variable gravity levels of up to 2-g. The temperature within the Specimen Chamber can be controlled between 4 and 45 C. Cabin air is recirculated within the Specimen Chamber and can be exchanged with the ISS cabin at a rate of approximately equal 50 cc/min. The humidity of the Specimen Chamber is monitored. The Specimen Chamber has a usable volume of approximately equal 19 liters and contains two (2) connectors at 28v dc, (60W) for science equipment; 5 dedicated thermometers for science; ports to support analog and digital signals from experiment unique sensors or other equipment; an Ethernet port; and a video port. It is currently manifested for UF-3 and will be launched integrated within the first SSBRP Habitat Holding Rack.

Nakamura, G. J.↗

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 Tech Briefs, May 2012

Topics covered include: An "Inefficient Fin" Non-Dimensional Parameter to Measure Gas Temperatures Efficiently; On-Wafer Measurement of a Multi-Stage MMIC Amplifier with 10 dB of Gain at 475 GHz; Software to Control and Monitor Gas Streams; Miniaturized Laser Heterodyne Radiometer (LHR) for Measurements of Greenhouse Gases in the Atmospheric Column; Anomaly Detection in Test Equipment via Sliding Mode Observers; Absolute Position of Targets Measured Through a Chamber Window Using Lidar Metrology Systems; Goldstone Solar System Radar Waveform Generator; Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System; Iridium Interfacial Stack - IrIS; Downsampling Photodetector Array with Windowing; Optical Phase Recovery and Locking in a PPM Laser Communication Link; High-Speed Edge-Detecting Line Scan Smart Camera; Optical Communications Channel Combiner; Development of Thermal Infrared Sensor to Supplement Operational Land Imager; Amplitude-Stabilized Oscillator for a Capacitance-Probe Electrometer; Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data; Histogrammatic Method for Determining Relative Abundance of Input Gas Pulse; Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH; LEGION: Lightweight Expandable Group of Independently Operating Nodes; Real-Time Projection to Verify Plan Success During Execution; Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data; Web-Based Customizable Viewer for Mars Network Overflight Opportunities; Fabrication of a Cryogenic Terahertz Emitter for Bolometer Focal Plane Calibrations; Fabrication of an Absorber-Coupled MKID Detector; Graphene Transparent Conductive Electrodes for Next- Generation Microshutter Arrays; Method of Bonding Optical Elements with Near-Zero Displacement; Free-Mass and Interface Configurations of Hammering Mechanisms; Wavefront Compensation Segmented Mirror Sensing and Control; Long-Life, Lightweight, Multi-Roller Traction Drives for Planetary Vehicle Surface Exploration; Reliable Optical Pump Architecture for Highly Coherent Lasers Used in Space Metrology Applications; Electrochemical Ultracapacitors Using Graphitic Nanostacks; Improved Whole-Blood-Staining Device; Monitoring Location and Angular Orientation of a Pill; Molecular Technique to Reduce PCR Bias for Deeper Understanding of Microbial Diversity; Laser Ablation Electrodynamic Ion Funnel for In Situ Mass Spectrometry on Mars; High-Altitude MMIC Sounding Radiometer for the Global Hawk Unmanned Aerial Vehicle; PRTs and Their Bonding for Long-Duration, Extreme-Temperature Environments; Mid- and Long-IR Broadband Quantum Well Photodetector; 3D Display Using Conjugated Multiband Bandpass Filters; Real-Time, Non-Intrusive Detection of Liquid Nitrogen in Liquid Oxygen at High Pressure and High Flow; Method to Enhance the Operation of an Optical Inspection Instrument Using Spatial Light Modulators; Dual-Compartment Inflatable Suitlock; Large-Strain Transparent Magnetoactive Polymer Nanocomposites; Thermodynamic Vent System for an On-Orbit Cryogenic Reaction Control Engine; Time Distribution Using SpaceWire in the SCaN Testbed on ISS; and Techniques for Solution- Assisted Optical Contacting.

Source record↗

Cleanroom Microbes Survive Drying, Vacuum, and Proton Irradiation

Introduction : The goal of planetary protection at NASA is to mitigate the risk of contaminating sensitive target bodies with biological life. While many cleaning procedures have been put in place to reduce bioburden on spacecraft, microbes are experts at evolving to survive harsh conditions. Specifically, the dry, low-nutrient environment of a cleanroom (commonly used for assembly of spacecraft) can represent an environment where extremophiles can survive. Methods : Scientists at NASA MSFC wished to gather a snapshot of the microbial population within a variety of cleanrooms on site. A study was undertaken to collect air, surface, and floor samples from clean-rooms and isolate unique morphologies. From this study, 95 isolates were collected and saved in a microbial library. About 86% of these were identified at least to a genus level. Following identification, 24 microbes were selected, based on a literature review, as potential extremophiles. These were grown in liquid cultures, diluted to a set optical density, washed with water, and then applied to a sterilized Kapton coupon. Droplets were allowed to dry overnight in a biosafety cabinet. Coupons were then installed in a pelletron and pumped down to high vacuum (~1E-6 Torr). Samples were then subjected 100 keV protons at a fluence of 2x10 15 p+/cm 2 up to 4x10 15 p+/cm 2 . Following exposure, samples were returned to the microbiology lab where they were pro-cessed by submerging in water, vortexing, and then plating either droplets or spread plates. Recovery data collected was qualitative with a ranking or +, minor, or – for growth. Some selected radiotolerant strains were sequenced using the Illumina sequencing platform. The resulting genomes were annotated with the Rapid Annotations using Subsystems Technology (RAST) server and analyzed for conserved and unique stress response relevant genomic signatures to identify clues related to specific tolerances. Results and Discussion : After five rounds of proton radiation, we narrowed our isolates to five, non-spore forming bacteria that demonstrated survival: Arthrobacter koreensis, Paenarthrobacter nitroguajacolicus, Mycetocola manganoxydans , and an Erwinia sp. Furthermore, we exposed these four microbes to 254 nm wavelength light at an intensity of 80 W/m 2 at a distance of ~18 cm for 10 minutes. Only A. koreensis demonstrated survival following UV exposure. Finally, we performed whole genome sequencing on the four strains to look for genetic markers of stress resistance. When we compared the genomes of the four strains, we found that genes coding for GGDEF and EAL domains with PAS/PAC sensors were only found in A. koreensis . These domains, modulated by PAS/PAC sensors, are hypothesized to facilitate survival under drying, desiccation, and proton irradiation. Drying and Desiccation : PAS domains sense hydration changes and modulate GGDEF and EAL domain activity to adjust c-di-GMP levels, enhancing resistance to desiccation. For instance, in Pseudomonas aeruginosa , the PAS domain of RbdA modulates activity under varying hydration conditions, affecting stress responses [1]. Proton Irradiation : Proton irradiation causes oxidative stress, leading to ROS generation. PAS domains detect this stress and modulate GGDEF and EAL domains to manage oxidative stress responses. In Shewanella , EAL domain proteins modulated by PAS sensors help bacteria adapt to extreme conditions [2]. These genes upregulate other stress response genes, protecting membrane function, protein stability, DNA repair, and antioxidant defenses. The modulation of c-di-GMP by PAS domains is crucial for bacterial adaptation to stress conditions, enabling dynamic physio-logical adjustments [3]. Understanding these mechanisms provides insights into bacterial stress responses and strategies for controlling bacterial growth [4]. Conclusions : These findings indicate that clean-rooms harbor extremophile microbes that may be able to survive conditions in deep space. Furthermore, while we identified certain stress-response genes that may be at least partly responsible for the phenotypes observed in this study, there are likely unidentified genes or characteristics about A. koreensis , and other bacteria, that may allow them to survive in harsh environments. Future studies will focus on identifying these unknown genes and characteristics, further elucidating the mechanisms of extremophile survival and potentially informing the development of new biotechnologies for space exploration and other extreme environments.

Chelsi Cassilly↗

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗