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The paper presents and compares two approaches to design fault-tolerant evolvable hardware: one based on the fitness definition and the other based on the population statistics.
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may benefit from a multiprocessor implementation, considering, on one hand, that analyses of individual designs in a population are independent of each other so that they may be executed concurrently on separate processors, and, on the other hand, that there are some operations in a GA that cannot be so distributed. The algorithm experimented with was based on a gaussian distribution rather than bit exchange in the GA reproductive mechanism, and the test case was a hub frame structure of up to 1080 design variables. The experimentation engaging up to 128 processors confirmed expectations of radical elapsed time reductions comparing to a conventional single processor implementation. It also demonstrated that the time spent in the non-distributable parts of the algorithm and the attendant cross-processor communication may have a very detrimental effect on the efficient utilization of the multiprocessor machine and on the number of processors that can be used effectively in a concurrent manner. Three techniques were devised and tested to mitigate that effect, resulting in efficiency increasing to exceed 99 percent.
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may benefit from a multiprocessor implementation, considering, on one hand, that analyses of individual designs in a population are independent of each other so that they may be executed concurrently on separate processors, and, on the other hand, that there are some operations in a GA that cannot be so distributed. The algorithm experimented with was based on a gaussian distribution rather than bit exchange in the GA reproductive mechanism, and the test case was a hub frame structure of up to 1080 design variables. The experimentation engaging up to 128 processors confirmed expectations of radical elapsed time reductions comparing to a conventional single processor implementation. It also demonstrated that the time spent in the non-distributable parts of the algorithm and the attendant cross-processor communication may have a very detrimental effect on the efficient utilization of the multiprocessor machine and on the number of processors that can be used effectively in a concurrent manner. Three techniques were devised and tested to mitigate that effect, resulting in efficiency increasing to exceed 99 percent.
Populations of Escherichia coli Z905/pPHL7, a transgenic microorganism, were heterogenic in the expression of plasmid genes when adapting to the conditions of water microcosms of various mineralization levels and structure of microbial community. This TM has formed two subpopulations (ampicillin-resistant and ampicillin-sensitive) in every microcosm. Irrespective of mineralization level of a microcosm, when E. coli Z905/pPHL7 alone was introduced, the ampicillin-resistant subpopulation prevailed, while introduction of the TM together with indigenous bacteria led to the dominance of the ampicillin-sensitive subpopulation. A high level of lux gene expression maintained longer in the freshwater microcosms than in sterile saline lake water microcosms. A horizontal gene transfer has been revealed between the jointly introduced TM and Micrococcus sp. 9/pSH1 in microcosms with the Lake Shira sterile water. c2005 COSPAR. Published by Elsevier Ltd. All rights reserved.
In this manuscript we address the challenges associated with the ability to predict radiation sensitivity associated with exposure to either cosmic radiation or X-rays in a population study, by monitoring DNA damage sensing protein 53BP1 forming small nuclear radiation-induced foci (RIF) as a surrogate biomarker of DNA double strand breaks (DSB). 76 primary skin fibroblasts were isolated from 10 collaborative cross strains and five reference inbred mice (C57Bl/6, BALB/CByJ, B6C3, C3H and CBA/CaJ) and exposed to three different charged nuclei of increasing LET (350 MeV/n Si, 350 MeV/n Ar and 600 MeV/n Fe) and X-ray. Our data brings strong evidence against the classic "contact-first" model where DSBs are assumed to be immobile and repaired at the lesion site. In contrast, our model suggests nearby DSBs move into single repair unit characterized by large RIF before the repair machinery kicks in. Such model has the advantage of being much more efficient molecularly but is poorly suited to deal with cosmic radiation, where energy is concentrated along the particle trajectory, inducing a large density of DSBs along each particle track. In accordance with this model, RIF quantification after X-ray exposition showed a saturated dose response for early time points post-irradiation for all strains. Similarly, the high-LET response showed that RIF number matched the number of track per cell, not the number of expected DSB per cell (1). At the temporal level, we noted that the percentage of unrepaired high-LET tracks over a 48 hour time-course increased with LET, confirming that the DNA repair process becomes more difficult as more DSB coalesce into single RIF. There was also good agreement between persistent RIF levels measured in-vitro in the primary skin cultures and survival levels of T-cells and B-cells collected in blood samples from 10 CC strains 24 hours after 0.1 Gy whole-body dose of X-ray. This suggests that persistent RIF 24 hour post-IR is a good surrogate in-vitro biomarker for in-vivo radiation toxicity. Finally, at the genomic level, large differences in repair rates between strains for high-LET allowed us to identify suggestive genetic loci associated with radiation sensitivity. Interestingly, the two highest LETs provided the most strain variation with a common locus on Chromosome 10 highly enriched for DNA repair associated genes we discussed in detail.
Maize rough endosperm (rgh) mutants have defective seeds with a rough, etched, or pitted endosperm surface. Molecular genetic analysis of this mutant class has identified multiple RNA processing proteins critical to endosperm development. We isolated the rgh6 locus from the UniformMu transposon tagging population. Mutant kernels have reduced grain-fill with defective embryos that fail to germinate. Self-pollination of rgh6 heterozygotes produces mutant seeds at frequency consistent with a single recessive mutation. Molecular mapping localized rgh6 to a 60 kbp interval on chromosome 5. PCR analysis of gene models within the fine-map interval identified a Mutator (Mu) transposon insertion within a predicted DEAD-box RNA helicase gene. A second allele of rgh6 was identified from UniformMu population. Reciprocal crosses between rgh6-umu1 and rgh6-umu2 heterozygotes produces mutant seeds at a frequency consistent with a 3:1 ratio indicating these mutants are allelic. Both rgh6-umu1 and rgh6-umu2 have Mu element insertions at the same insertion site within the DEAD-box RNA helicase gene. However, the terminal inverted repeat sequences are polymorphic between the alleles showing the alleles are independent mutations. We conclude that mutation of the DEAD-box RNA helicase causes the rgh6 phenotype. Further characterization of the rgh6 mutant phenotype and biochemical investigation of the RGH6 protein is expected to give further insights on the roles of RNA processing in endosperm development.
The genetic relationship between short-period comets and meteor streams is investigated. It is shown that mechanisms exist for the radial and the longitudinal focussing of particles in meteor streams with characteristic time scales of agglomeration significantly smaller than those of any of the known dispersive processes. Consequently, it is claimed that meteor streams may not merely form a sink for short-period comets but may also form a source. A likely origin for the volatiles observed in such comets is suggested. It is finally stressed that this reciprocity in the genetic relationship between short-period comets and meteor streams should form an important consideration in any attempt at accounting for the observed population of short-period comets.
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
Contaminant terrestrial microbiota could profoundly impact the scientific integrity of extraterrestrial life-detection experiments. It is therefore important to know what organisms persist on spacecraft surfaces so that their presence can be eliminated or discriminated from authentic extraterrestrial biosignatures. Although there is a growing understanding of the biodiversity associated with spacecraft and cleanroom surfaces, it remains challenging to assess the risk of these microbes confounding life-detection or sample-return experiments. A key challenge is to provide a comprehensive inventory of microbes present on spacecraft surfaces. To assess the phylogenetic breadth of microorganisms on spacecraft and associated surfaces, the Genetic Inventory team used three technologies: conventional cloning techniques, PhyloChip DNA microarrays, and 454 tag-encoded pyrosequencing, together with a methodology to systematically collect, process, and archive nucleic acids. These three analysis methods yielded considerably different results: Traditional approaches provided the least comprehensive assessment of microbial diversity, while PhyloChip and pyrosequencing illuminated more diverse microbial populations. The overall results stress the importance of selecting sample collection and processing approaches based on the desired target and required level of detection. The DNA archive generated in this study can be made available to future researchers as genetic-inventory-oriented technologies further mature.
We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems. It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.
Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study was to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts were recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests included assessments of sensory dependency and adaptability. Visual dependency involved treadmill walking while viewing a moving virtual visual scene. Vestibular perceptual thresholds were measured while seated during lateral translations. Proprioception dependency was measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability was performed (1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms (adaptive Functional Mobility Test, aFMT). The neuroimaging tests characterized individual differences in regional brain volumes (using Structural MRI) and white matter microstructure (using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests utilized saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Thirty ISS crewmembers were recruited, including 12 first-time fliers, 6F, and mission durations lasting 185.5 ± 45.5 days, mean ± std. We utilized a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There was considerable variability among all post-flight performance outcomes. Based on an ordinal scale survey, 72% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. Understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.
COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.
The use of the microgravity environment to separate and purify biological cells and proteins has been a major activity since the beginning of the NASA Microgravity Science and Applications program. Purified populations of cells are needed for research, transplantation and analysis of specific cell constituents. Protein purification is a necessary step in research areas such as genetic engineering where the new protein has to be separated from the variety of other proteins synthesized from the microorganism. Sufficient data are available from the results of past electrophoresis experiments in space to show that these experiments were designed with incomplete knowledge of the fluid dynamics of the process including electrohydrodynamics. However, electrophoresis is still an important separation tool in the laboratory and thermal convection does limit its performance. Thus, there is a justification for electrophoresis but the emphasis of future space experiments must be directed toward basic research with model experiments to understand the microgravity environment and fluid analysis to test the basic principles of the process.
Meteoritic materials are investigated as potential early planetary nutrients. Aqueous extracts of the Murchison C2 carbonaceous meteorite are utilized as a sole carbon source by microorganisms, as demonstrated by the genetically modified Pseudomonas fluorescence equipped with the lux gene. Nutrient effects are observed also with the soil microorganisms Nocardia asteroides and Arthrobacter pascens that reach populations up to 5 x 10(7) CFU/ml in meteorite extracts, similar to populations in terrestrial soil extracts. Plant tissue cultures of Asparagus officinalis and Solanum tuberosum (potato) exhibit enhanced pigmentation and some enhanced growth when meteorite extracts are added to partial nutrient media, but inhibited growth when added to full nutrient solution. The meteorite extracts lead to large increases in S, Ca, Mg, and Fe plant tissue contents as shown by X-ray fluorescence, while P, K, and Cl contents show mixed effects. In both microbiological and plant tissue experiments, the nutrient and inhibitory effects appear to be best balanced for growth at about 1:20 (extracted solid : H2O) ratios. The results suggest that solutions in cavities in meteorites can provide efficient concentrated biogenic and early nutrient environments, including high phosphate levels, which may be the limiting nutrient. The results also suggest that carbonaceous asteroid resources can sustain soil microbial activity and provide essential macronutrients for future space-based ecosystems.
Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study is to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts are recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests include assessments of sensory dependency and adaptability. Visual dependency involves treadmill walking while viewing a moving virtual visual scene. Vestibular thresholds are measured while seated during lateral translations. Proprioception dependency is measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability is performed (1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms. The neuroimaging tests will characterize individual differences in regional brain volumes (using Structural MRI) and white matter microstructure (using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests will utilize saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Twenty-one ISS crewmembers have been tested to date, including 6 from this past year after testing resumed post-COVID. This cohort includes 9 first-time fliers, 4F, and mission durations lasting 182 ± 32 days, mean ± std. We are utilizing a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There is considerable variability among the post-flight performance outcomes for the 21 participants to date. Based on a partial sample using an ordinal scale survey, 80% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. There are apparent relationships between individual measures and specific post-flight outcome measures; however, additional data is needed to draw conclusions. Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. We expect that understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance will improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.
Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study is to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts are recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests include assessments of sensory dependency and adaptability. Visual dependency involves treadmill walking while viewing a moving virtual visual scene. Vestibular perceptual thresholds are measured while seated during lateral translations. Proprioception dependency is measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability is performed(1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms(adaptive Functional Mobility Test, aFMT). The neuroimaging tests will characterize individual differences in regional brain volumes (using Structural MRI) and white matter microstructure(using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests will utilize saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Twenty-seven ISS crewmembers have been tested to date, including 6 from this past year. This cohort includes 10first-time fliers, 6F, and mission durations lasting 178.6 ± 30.5 days, mean ± std. We are utilizing a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There is considerable variability among the post-flight performance outcomes for the 27participants to date. Based on a partial sample using an ordinal scale survey, 70% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. There are apparent relationships between individual measures and specific post-flight outcome measures, e.g., the cumulative time to complete the aFMT is significantly correlated to pre-to-post-flight changes in tandem walk (rho= 0.64, p = 0.001). Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. We expect that understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance will improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures
Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.
The Mice Drawer System (MDS) is an Italian Space Agency (ASI) facility which is able to support mice onboard the International Space Station during long-duration exploration missions (from 100 to 150-days) by living space, food, water, ventilation and lighting. Mice can be accommodated either individually (maximum 6) or in groups (4 pairs). MDS is integrated in the Space Shuttle middeck during transportation (uploading and downloading) to the ISS and in an EXPRESS Rack in Destiny, the US Laboratory during experiment execution. Osteoporosis is a debilitating disease that afflicts millions of people worldwide. One of the physiological changes experienced by astronauts during space flight is the accelerated loss of bone mass due to the lack of gravitational loading on the skeleton. This bone loss experienced by astronauts is similar to osteoporosis in the elderly population. MDS will help investigate the effects of unloading on transgenic (foreign gene that has been inserted into its genome to exhibit a particular trait) mice with the Osteoblast Stimulating Factor-1, OSF-1, a growth and differentiation factor, and to study the genetic mechanisms underlying the bone mass pathophysiology. MDS will test the hypothesis that mice with an increased bone density are likely to be more protected from osteoporosis, when the increased bone mass is a direct effect of a gene involved in skeletogenesis (skeleton formation). Osteoporosis is a debilitating disease that afflicts millions worldwide. One of the physiological changes experienced by astronauts during space flight is the accelerated loss of bone mass due to the lack of gravitational loading on the skeleton, a loss that is similar to osteoporosis in the elderly population on Earth. Osteoblast Stimulating Factor-1 (OSF-1), also known as pleiotrophin (PTN) or Heparin-Binding Growth- Associated Molecule (HB-GAM) belongs to a family of secreted heparin binding proteins..OSF-1 is an extracellular matrix-associated growth and differentiation factor that is normally expressed in cartilage; it can stimulate the proliferation and differentiation of human osteoprogenitor cells (cell that differentiate into an osteoblast) in vitro. The Mice Drawer System will study the effects of microgravity on transgenic mouse bones in order to identify genetic mechanisms playing a role in the reduction of the bone mass observed in humans and animals as a consequence of long-duration (greater than 100 days) microgravity exposure. Onboard the ISS, MDS is relatively self-sufficient; a crewmember will check the health status of the rodents on a daily basis, by assessing them through the viewing window. Water levels will be assessed by the crew daily and refilled as needed. Replacement of the food bars and replacement of the waste filters will be conducted inflight by crewmembers every 20-days.