Control in biological systems - a physical review.
Regulation and control in biological systems modeling body by dynamic networks
BIOLOGICAL RHYTHM↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Some mechanisms for a theory of the reticular formation Final report, 15 Nov. 1965 - 14 Nov. 1966
Nonlinear, probabilistic hybrid computer concepts for specifying operational schemata of central nervous system models
PROBABILITY THEORY↗
Mechanism of rapid suppression of cell expansion in cucumber hypocotyls after blue-light irradiation
Rapid suppression of hypocotyl elongation by blue light in cucumber (Cucumis sativus L.) was studied to examine possible hydraulic and wall changes responsible for diminished growth. Cell-sap osmotic pressure, measured by vapor-pressure osmometry, was not decreased by blue light; turgor pressure, measured by the pressure-probe technique, remained constant during the growth inhibition; and stem hydraulic conductance, measured by dynamic and static methods, was likewise unaffected by blue light. Wall yielding properties were assessed by the pressure-block technique for in-vivo stress relaxation. Blue light reduced the initial rate of relaxation by 77%, but had little effect on the final amount of relaxation. The results demonstrate that blue irradiation acts to decrease the wall yielding coefficient, but not the yield threshold. Stress-strain (Instron) analysis showed that irradiation of the seedlings had little effect on the mechanical extensibilities of the isolated wall. The results indicate that blue light can reduce cell-wall loosening without affecting bulk viscoelastic properties, and indicate a chemorheological mechanism of cell-wall expansion.
NASA Discipline Number 40-30↗
Wall relaxation and the driving forces for cell expansive growth
When water uptake by growing cells is prevented, the turgor pressure and the tensile stress in the cell wall are reduced by continued wall loosening. This process, termed in vivo stress relaxation, provides a new way to study the dynamics of wall loosening and to measure the wall yield threshold and the physiological wall extensibility. Stress relaxation experiments indicate that wall stress supplies the mechanical driving force for wall yielding. Cell expansion also requires water absorption. The driving force for water uptake during growth is created by wall relaxation, which lowers the water potential of the expanding cells. New techniques for measuring this driving force show that it is smaller than believed previously; in elongating stems it is only 0.3 to 0.5 bar. This means that the hydraulic resistance of the water transport pathway is small and that rate of cell expansion is controlled primarily by wall loosening and yielding.
NASA Program Space Biology↗
Innovation and application of ANN in Europe demonstrated by Kohonen maps
One of the most important contributions to neural networks comes from Kohonen, Helsinki/Espoo, Finland, who had the idea of self-organizating maps in 1981. He verified his idea by an algorithm of which many applications make use of. The impetus for this idea came from biology, a field where the Europeans have always been very active at several research laboratories. The challenge was to model the self-organization found in the brain. Today one goal is the development of more sophisticated neurons which model the biological neurons more exactly. They should come to a better performance of neural nets with only a few complex neurons instead of many simple ones. A lot of application concepts arise from this idea: Kohonen himself applied it to speech recognition, but the project did not overcome much more than the recognition of the numerals one to ten at that time. A more promising application for self-organizing maps is process control and process monitoring. Several proposals were made which concern parameter classification of semiconductor technologies, design of integrated circuits, and control of chemical processes. Self-organizing maps were applied to robotics. The neural concept was introduced into electric power systems. At Dortmund we are working on a system which has to monitor the quality and the reliability of gears and electrical motors in equipment installed in coal mines. The results are promising and the probability to apply the system in the field is very high. A special feature of the system is that linguistic rules which are embedded in a fuzzy controller analyze the data of the self-organizing map in regard to life expectation of the gears. It seems that the fuzzy technique will introduce the technology of neural networks in a tandem mode. These technologies together with the genetic algorithms start to form the attractive field of computational intelligence.
Goser, Karl↗
Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
James Casaletto↗
Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
James Andrew Casaletto↗
Microbial burden prediction model program
Model supplements biological surveys of spacecraft by simulating microbial burden accumulation process during periods when surveys are not taken. Important application of model is to predict microbial loading on spacecraft landing capsule immediately prior to terminal heat sterilization.
Meuser, R. F.↗
Evolutionary dynamics of plants and animals: a comparative approach
Patterns of longevity and rate of appearance of taxa in the fossil record indicate a different evolutionary dynamic between land plants and marine invertebrates. Among marine invertebrates, rates of taxonomic turnover declined through the Phanerozoic, with increasingly extinction-resistant, long-lived, clades coming to dominate. Among terrestrial vascular plants, rates of turnover increased through the Phanerozoic, with short-lived, extinction-prone clades coming to dominate from the Devonian to the present. Terrestrial vertebrates appear to approximate the marine invertebrate pattern more closely than the plant record. We identify two features which individually or jointly may have influenced this distinction. First, land plants continuously invaded stressful environments during their evolution, while marine invertebrates and terrestrial vertebrates did not. Second, the relative structural simplicity and indeterminate mode of plant growth vs. the relative structural complexity and determinate mode of animal growth may have influenced the timing of major clade origin in the two groups.
Review↗
TransPlatformer
We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands . Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches. The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization. In this repository there are code examples for TransPlatformer implementation
Cong, Guojing↗
Nonlinear and time-varying dynamical models of human operators in manual control systems.
Nonlinear and time-varying dynamical models of human operators in manual control systems
BIOLOGICAL MODEL↗
Evolutionary and preservational constraints on origins of biologic groups: divergence times of eutherian mammals
Some molecular clock estimates of divergence times of taxonomic groups undergoing evolutionary radiation are much older than the groups' first observed fossil record. Mathematical models of branching evolution are used to estimate the maximal rate of fossil preservation consistent with a postulated missing history, given the sum of species durations implied by early origins under a range of species origination and extinction rates. The plausibility of postulated divergence times depends on origination, extinction, and preservation rates estimated from the fossil record. For eutherian mammals, this approach suggests that it is unlikely that many modern orders arose much earlier than their oldest fossil records.
Non-NASA Center↗
Random root movements in weightlessness
The dynamics of root growth was studied in weightlessness. In the absence of the gravitropic reference direction during weightlessness, root movements could be controlled by spontaneous growth processes, without any corrective growth induced by the gravitropic system. If truly random of nature, the bending behavior should follow so-called 'random walk' mathematics during weightlessness. Predictions from this hypothesis were critically tested. In a Spacelab ESA-experiment, denoted RANDOM and carried out during the IML-2 Shuttle flight in July 1994, the growth of garden cress (Lepidium sativum) roots was followed by time lapse photography at 1-h intervals. The growth pattern was recorded for about 20 h. Root growth was significantly smaller in weightlessness as compared to gravity (control) conditions. It was found that the roots performed spontaneous movements in weightlessness. The average direction of deviation of the plants consistently stayed equal to zero, despite these spontaneous movements. The average squared deviation increased linearly with time as predicted theoretically (but only for 8-10 h). Autocorrelation calculations showed that bendings of the roots, as determined from the 1-h photographs, were uncorrelated after about a 2-h interval. It is concluded that random processes play an important role in root growth. Predictions from a random walk hypothesis as to the growth dynamics could explain parts of the growth patterns recorded. This test of the hypothesis required microgravity conditions as provided for in a space experiment.
STS-65 Shuttle Project↗
Temperature regulation in man - a theoretical study.
Model of thermoregulation in man
BODY TEMPERATURE REGULATION↗
Volume 35, AMT-1 Cruise Report and Preliminary Results
This report documents the scientific activities on board the Royal Research Ship (RRS) 'James Clark Ross' during the irst Atlantic Meridional Transect (AMT-1), 21 September to 24 October 1995. The ship sailed from Grimsby (England) for Montevideo (Uruguay) and then continued on to Stanley (Falkland Islands). The primary objective of the AMT program is to investigate basic biological processes in the open Atlantic Ocean over very broad spatial scales. For AMT-1, the meridional range covered was approximately 50 deg N to 50 deg S or nearly 8,000 nmi. The measurements to be taken during the AMT cruises are fundamental for the calibration, validation, and continuing understanding of remotely sensed observations of biological oceanography. They are also important for understanding plankton community structure over latitudinal scales and the role of the world ocean in global carbon cycles. During AMT-1 a variety of instruments were used to map the physical, chemical, and biological structure of the upper 200 m of the water column. Ocean color measurements were made using state-of-the-art sensors, whose calibration was traceable to the highest international standards. New advances in fluorometry were used to measure photosynthetic activity, which was then used to further interpret primary productivity. A unique set of samples and data were collected for the planktonic assemblages that vary throughout the range of the transect. These data will yield new interpretations on community composition and their role in carbon cycling. While the various provinces of the Atlantic Ocean were being crossed, the partial pressure of CO2 was related to biological productivity. This comparison revealed the areas of drawdown of atmospheric CO2 and how these areas relate to the surrounding biological productivity. These data, plus the measurements of light attenuation and phytoplankton optical properties, will be used as a primary input for basin-scale biological productivity models to help develop ecosystem dynamics models which will be important for improving the forecasting abilities of modelers. The AMT program is also attempting to meet the needs of international agencies in their implementation of Sensor Intercomparison and Merger for Biological and Interdisciplinary Ocean Studies (SIMBIOS), a program to develop a methodology and operational capability to combine data products from the various ocean color satellite missions.
Hooker, Stanford B.↗
Vestibular receptor cells and signal detection: bioaccelerometers and the hexagonal sampling of two-dimensional signals
The inner ear contains sensory organs which signal changes in head movement. The vestibular sacs, in particular, are sensitive to linear accelerations. Electron microscopic images have revealed the structure of tiny sensory hair bundles, whose mechanical deformation results in the initiation of neuronal activity and the transmission of electrical signals to the brain. The structure of the hair bundles is shown in this paper to be that of the most efficient two-dimensional phased-array signal processors.
NASA Program Space Physiology and Countermeasures↗
Detection in fixed and random noise in foveal and parafoveal vision explained by template learning
Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.
NASA Center ARC↗