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855 results for “model system”
MicroRNA Signature of Central Nervous System-Infiltrating Dendritic Cells in an Animal Model of Multiple Sclerosis
GEO Series GSE111256. Mus musculus. 8 samples. Type: Expression profiling by array.
A humanized neuronal model system reveals key roles for the trace element manganese in neuronal endocytosis, calcium flux and mitochondrial bioenergetics
GEO Series GSE266943. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Process interoperability between biodiversity modeling systems: Engineering of complex systems.
Open the record for dataset details and reuse information.
Transcriptomics of heart and muscle tissues of transgenic or mdx mice (DMD disease model) with UAA incorporation systems
GEO Series GSE158961. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease
GEO Series GSE141509. Mus musculus. 155 samples. Type: Expression profiling by array.
Inhibition of HDAC1/2 along with TRAP1 causes Synthetic Lethality in Glioblastoma Model Systems
GEO Series GSE150395. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Induction of systemic disease-specific salivary biomarker profiles in mouse cancer models
GEO Series GSE13443. Mus musculus. 27 samples. Type: Expression profiling by array.
Modeling the clinical phenotype of BTK inhibition in the mature murine immune system
GEO Series GSE56483. Mus musculus. 18 samples. Type: Expression profiling by array.
A model system for assessing the ability of exon microarray and tag sequencing to detect genes specific for malignant B-cells
GEO Series GSE40311. Homo sapiens. 16 samples. Type: Expression profiling by array; Expression profiling by high throughput sequencing.
Targeted inhibition of human breast cancer cell line selected as model system of ERBB2-positive/EGFR high breast cancer [RPPA-Shortterm-HCC1954]
GEO Series GSE50105. Homo sapiens. 9 samples. Type: Protein profiling by protein array.
Probabilistic Model-Based Diagnosis for Electrical Power Systems
We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
Land Information System for SMAP Tier-1 and AirMOSS Earth Venture-1 Decadal Survey Missions: Integration of SoilSCAPE, remote sensing, and modeling Project
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Model-Based Diagnosis and Prognosis of a Water Recycling System
A water recycling system (WRS) deployed at NASA Ames Research Center’s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned out- ages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.
Aviation System Monitoring and Modeling Project
Air transportation, one of the most important modes of transportation, is also one of the safest. Nevertheless, the public demands that safety levels continuously improve and that the absolute number of aviation accidents continue to decline, even as air-traffic levels increase. On February 12, 1997, after the tragedy of TWA 800, President William J. Clinton declared, “We will achieve a national goal of reducing the fatal aircraft accident rate by 80% within 10 years.” In response to this presidential declaration, the administrator of NASA announced that NASA would undertake a new program in aviation safety in support of this objective. NASA quickly formed the Aviation Safety Investment Strategy Team (ASIST) in collaboration with the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB), which organized a series of five workshops to examine the options and recommend an approach for NASA to develop the enabling technologies that address the president's goal. The exceptional and dedicated participation from all sectors of the aviation industry in the development of NASA’s strategy was remarkable.
Modeling cellular responses to serum and vitamin D in microgravity using a human kidney microphysiological system
The microgravity environment aboard the International Space Station (ISS) provides a unique stressor that can help understand underlying cellular and molecular drivers of pathological changes observed in astronauts with the ultimate goals of developing strategies to enable long- term spaceflight and better treatment of diseases on Earth. We used this unique environment to evaluate the effects of microgravity on kidney proximal tubule epithelial cell (PTEC) response to serum exposure and vitaminD biotransformation capacity. To test if microgravity alters the pathologic response of the proximal tubule to serum exposure, we treated PTECs cultured in a microphysiological system (PT-MPS) with human serum and measured biomarkers of toxicity and inflammation (KIM-1 and IL-6) and conducted global transcriptomics via RNAseq on cells undergoing flight (microgravity) and respective controls(ground). Given the profound bone loss observed in microgravity and PTECs produce the active form of vitamin D, we treated 3D cultured PTECs with 25(OH)D 3 (vitamin D) and monitored vitamin D metabolite formation, conducted global transcriptomics via RNAseq, and evaluated transcript expression of CYP27B1, CYP24A1, or CYP3A5 in PTECs undergoing flight (microgravity) and respective ground controls. We demonstrated that microgravity neither altered PTEC metabolism of vitamin D nor did it induce a unique response of PTECs to human serum, suggesting that these fundamental biochemical pathways in the kidney proximal tubule are not significantly altered by short-term exposure to microgravity. Given the prospect of extended spaceflight, more study is needed to determine if these responses are consistent with extended (greater than 6 months) exposure to microgravity.
Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics
Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α − λ precision, β accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.
Model-Based Fault Detection and Diagnosis System for NASA Mars Subsurface Drill Prototype
The Drilling Automation for Mars Environment (DAME) project, led by NASA Ames Research Center, is aimed at developing a lightweight, low-power drill prototype that can be mounted on a Mars lander and be capable of drilling down several meters below the Mars surface for conducting geology and astrobiology research. The DAME drill system incorporates a large degree of autonomy - from quick diagnosis of system state and fault conditions to making the appropriate recovery actions - while also striving to achieve as many of the operational objectives as possible. This paper outlines, on a general level, the overall DAME architecture, equipment, and autonomy package. The main focus, however, is on describing the model-based fault detection and diagnosis system, including the modeling approach, the fault modes handled, and the diagnostic algorithms. The results of the latest field tests, conducted in 2006 in Haughton Crater on Devon Island (a Mars analogue site in Canadian Arctic), are also discussed.
A Structural Model Decomposition Framework for Systems Health Management
Systems health management (SHM) is an impor- tant set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL predic- tion. We solve these model decomposition problems using a three-tank system as a case study.
A knowledge-based system approach for sensor fault modeling, detection and mitigation
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
Model-based Diagnostics for Propellant Loading Systems
The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that recov- ery actions can be taken or an abort procedure can be initi- ated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physi- cal processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their ef- fects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which de- scribes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the in- fluence of different faults on this process. The model takes into account the main physical processes such as highly non- equilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydro- gen and vapor flows inside the system in the presence of he- lium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based di- agnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and per- form fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.