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Session MP2 includes short reports on: (1) Crew Regenerative Life Support in Long Duration Space Missions; (2) Bioconversion Systems for Food and Water on Long Term Space Missions; (3) Novel Laboratory Approaches to Multi-purpose Aquatic Biogenerative Closed-Loop Food Production Systems; and (4) Artificial Neural Network Derived Plant Growth Models.
SVET Space Greenhouse (SG)--the first automated facility for growing of higher plants in microgravity was designed in the eighty years to be used for the future BLSS. The first successful experiment with vegetables was carried out in 1990 on the MIR Space Station (SS). The experiments in SVET SG were resumed in 1995, when an American Gas Exchange Measurement System (GEMS) was added. A three-month wheat experiment was carried out as part of MIR-SHUTTLE'95 program. SVET-2 SG Bulgarian equipment of a new generation with optimised characteristics was developed (financed by NASA). The new SVET-GEMS equipment was launched on board the MIR SS and a successful six-month experiments for growing up of two crops of wheat were conducted in 1996 - 97 as part of MIR-NASA-3 program. The first of these "Greenhouse" experiments (123 days) with the goal to grow wheat through a complete life cycle is described. Nearly 300 heads developed but no seeds were produced. A second crop of wheat was planted and after 42 days the plants were frozen for biochemical investigations. The main environmental parameters during the six-month experiments in SVET (substrate moisture and lighting period) are given. The results and the contribution to BLSS are discussed.
A perspective of the process of obtaining performance robustness of space exploration life support systems is presented. Some useful definitions are made, some relevant issues are addressed, and a procedure for establishing performance robustness, so far as it now known, is explained. An example is given to illustrate the procedure.
High-throughput amplicon sequencing of the ITS, 16S and 18S regions from soil, roots and leaves DNAs of the 50 plots present in the BCSE (Biofuel Cropping System Experiment) experiment at Kellogg Biological Station.
Geometric frustration results from an incompatibility between minimum energy arrangements and the geometry of a system, and gives rise to interesting and novel phenomena. Here, we report geometric frustration in a native biological macromolecular system---vertebrate muscle. We analyse the disorder in the myosin filament rotations in the myofibrils of vertebrate striated (skeletal and cardiac) muscle, as seen in thin-section electron micrographs, and show that the distribution of rotations corresponds to an archetypical geometrically frustrated system---the triangular Ising antiferromagnet. Spatial correlations are evident out to at least six lattice spacings. The results demonstrate that geometric frustration can drive the development of structure in complex biological systems, and may have implications for the nature of the actin--myosin interactions involved in muscle contraction. Identification of the distribution of myosin filament rotations with an Ising model allows the extensive results on the latter to be applied to this system. It shows how local interactions (between adjacent myosin filaments) can determine long-range order and, conversely, how observations of long-range order (such as patterns seen in electron micrographs) can be used to estimate the energetics of these local interactions. Furthermore, since diffraction by a disordered system is a function of the second-order statistics, the derived correlations allow more accurate diffraction calculations, which can aid in interpretation of X-ray diffraction data from muscle specimens for structural analysis.
Biologically‐based water recovery systems are a regenerative, low energy alternative to physiochemical processes to reclaim water from wastewater. This paper summarizes the results of the Alternative Water Processor (AWP) test conducted over one year. The AWP recovered 90% of water from four crewmembers using (4) membrane aerated bioreactors (MABRs) to remove carbon and nitrogen from an exploration mission wastewater, including urine, hygiene, laundry and humidity condensate. Downstream, a coupled forward and reverse osmosis system removed large organics and inorganic salts from the biological system effluent. The system exceeded the overall objectives of the test by recovering 90% of the influent wastewater processed and a 29% reduction of consumables from the current state of the art water recovery system on the International Space Station (ISS). However the biological system fell short of its test goals, failing to remove 75% and 90% of the influent ammonium and organic carbon, respectively. Despite not meeting its test goals, the BWP demonstrated the feasibility of an attachedgrowth biological system for simultaneous nitrification and denitrification, an innovative, volume and consumable‐saving design that doesn't require toxic pretreatment. This paper will explain the reasons for this and will discuss steps to optimize each subsystem to increase effluent quality from the MABRs and the FOST to advance the system.
Biologically-based water recovery systems are a regenerative, low energy alternative to physiochemical processes to reclaim water from wastewater. This paper summarizes the results of the Alternative Water Processor (AWP) test conducted over one year. The AWP recovered 90% of water from four crewmembers using (4) membrane aerated bioreactors (MABRs) to remove carbon and nitrogen from an exploration mission wastewater, including urine, hygiene, laundry and humidity condensate. Downstream, a coupled forward and reverse osmosis system removed large organics and inorganic salts from the biological system effluent. The system exceeded the overall objectives of the test by recovering 90% of the influent wastewater processed and a 29% reduction of consumables from the current state of the art water recovery system on the International Space Station (ISS). However the biological system fell short of its test goals, failing to remove 75% and 90% of the influent ammonium and organic carbon, respectively. Despite not meeting its test goals, the BWP demonstrated the feasibility of an attached-growth biological system for simultaneous nitrification and denitrification, an innovative, volume and consumable-saving design that doesn't require toxic pretreatment. This paper will explain the reasons for this and will discuss steps to optimize each subsystem to increase effluent quality from the MABRs and the FOST to advance the system.
Anthropogenic perturbations to the nitrogen cycle, primarily through use of synthetic fertilizers, is driving an unprecedented increase in the emission of nitrous oxide (N 2 O), a potent greenhouse gas and an ozone depleting substance, causing urgency in identifying the sources and sinks of N 2 O. Microbial denitrification is a primary contributor to biotic production of N 2 O in anoxic regions of soil, marine systems, and wastewater treatment facilities. Here, through comprehensive genome analysis, we show that pathway partitioning is a ubiquitous mechanism of complete denitrification within microbial communities. We have investigated mechanisms and consequences of process partitioning of denitrification through detailed physiological characterization and kinetic modeling of a synthetic community of Rhodanobacter thiooxydans FW510-R12 and Acidovorax sp. GW101-3H11. We have discovered that these two bacterial isolates, from a heavily nitrate (NO 3 − ) contaminated superfund site, complete denitrification through the exchange of nitrite (NO 2 − ) and nitric oxide (NO). The process partitioning of denitrification and other processes, including amino acid metabolism, contribute to increased cooperativity within this denitrifying community. We demonstrate that certain contexts, such as high NO 3 − , cause unbalanced growth of community members, due to differences in their substrate utilization kinetics. The altered growth characteristics of community members drives accumulation of toxic NO 2 − , which disrupts denitrification causing N 2 O off gassing.
The two phase systems that result when aqueous solutions of dextran and poly(ethylene glycol) (PEG) are mixed above a critical concentration of a few percent provide a useful medium for the separation of biological cell subpopulations via partition between the top, PEG-rich phase and the liquid-liquid phase boundary. Interfacial tensions of such systems have been measured by the rotating drop technique and found to range between 0.1-100 micro-N/m. The tension was found to depend on the length of the tie line describing the system on a phase diagram, via a power law relationship which differed depending on the concentration of Na phosphate buffer present. The electrokinetic properties of drops of one phase suspended in the other were studied for a variety of systems. It was found that the droplet electrophoretic mobility increased monotonically with phosphate concentration and drop diameter but exhibited the opposite sign from that anticipated from phosphate partition measurements. It was possible to take advantage of these electrokinetic properties and dramatically enhance the speed of phase separation through application of relatively small electric fields.
Biologically-based water recovery systems are a regenerative, low energy alternative to physiochemical processes to reclaim water from wastewater. This report summarizes the results of the Alternative Water Processor (AWP) Integrated Test, conducted from June 2013 until April 2014. The system was comprised of four (4) membrane aerated bioreactors (MABRs) to remove carbon and nitrogen from an exploration mission wastewater and a coupled forward and reverse osmosis system to remove large organic and inorganic salts from the biological system effluent. The system exceeded the overall objectives of the test by recovering 90% of the influent wastewater processed into a near potable state and a 64% reduction of consumables from the current state of the art water recovery system on the International Space Station (ISS). However, the biological system fell short of its test goals, failing to remove 75% and 90% of the influent ammonium and organic carbon, respectively. Despite not meeting its test goals, the BWP demonstrated the feasibility of an attached-growth biological system for simultaneous nitrification and denitrification, an innovative, volume- and consumable-saving design that does not require toxic pretreatment.
Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.
Over the last several decades important information has been gathered by conducting life science experiments on the Space Shuttle and on the International Space Station. It is now time to leverage that scientific knowledge, as well as aspects of the hardware that have been developed to support the biological model systems, to NASA's next frontier - the Deep Space Gateway. In order to facilitate long duration deep space exploration for humans, it is critical for NASA to understand the effects of long duration, low dose, deep space radiation on biological systems. While carefully controlled ground experiments on Earth-based radiation facilities have provided valuable preliminary information, we still have a significant knowledge gap on the biological responses of organisms to chronic low doses of the highly ionizing particles encountered beyond low Earth orbit. Furthermore, the combined effects of altered gravity and radiation have the potential to cause greater biological changes than either of these parameters alone. Therefore a thorough investigation of the biological effects of a cis-lunar environment will facilitate long term human exploration of deep space.
The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).
Equilibrium and non-equilibrium systems in pre-biological atmospheres
The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.
Bioplastics have emerged as a tangible solution to the plastic waste crisis.