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6,383
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6,383 results for “isolates”
The aldosterone-induced transcriptome of isolated mouse collecting duct cells
GEO Series GSE122995. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Isolated TEC and DC from human iPSc-derived thymic organoids (hTOs) seeded with primary early thymic progenitors (ETPs) at late phase of maturation (D28)
GEO Series GSE294061. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
['"Draft Genome Sequences of Sphingomonas sp. Isolated from the International Space Station Genome sequencing and assembly"']
['"Draft Genome Sequences of Sphingomonas sp. Isolated from the International Space Station"']
Using Decision Trees to Detect and Isolate Leaks in the J-2X
**Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine** Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center **Abstract** The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. **Introduction** The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool [http://www.gensym.com](http://www.gensym.com) to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro
Population transcriptional profiling of Th17 cells, isolated from the lamina propria of the large intestine from 3-6 month old IL-17GFP KI mice
GEO Series GSE75106. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Gene expression in AES-2 clonal isolates of Pseudomonas aeruginosa
GEO Series GSE10304. Pseudomonas aeruginosa. 8 samples. Type: Expression profiling by array.
Transcriptional response of an azole-resistant Candida parapsilosis isolate [posaconazole].
GEO Series GSE27407. Candida parapsilosis. 3 samples. Type: Expression profiling by array.
Changes in mammary gene expression and morphology following consumption of soy protein isolate in female Sprague-Dawley rats differs from that produced by 17b-estradiol treatment
GEO Series GSE38060. Rattus norvegicus. 12 samples. Type: Expression profiling by array.
Genomic comparisons of Salmonella enterica serovar Paratyphi A isolates
GEO Series GSE1500. Salmonella enterica; Salmonella enterica subsp. enterica serovar Paratyphi A. 36 samples. Type: Genome variation profiling by array.
Gene expression data of stromal cell subpopulations isolated from human lymph nodes (LNs) infiltrated with melanoma
GEO Series GSE150965. Homo sapiens. 14 samples. Type: Expression profiling by array.
Expression data from mesenchymal stem and progenitor cells freshly isolated from human bone marrow
GEO Series GSE171654. Homo sapiens. 11 samples. Type: Expression profiling by array.
ATAC-seq profiling in CD14+ cells isolated from rhesus macaques vaccinated with V1-deleted DNA/ALVAC/gp120 vaccine [study 2]
GEO Series GSE188879. Macaca mulatta. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Expression data of enterotoxigenic (ETEC) isolate E24377A
GEO Series GSE31804. Shigella; Escherichia coli O139:H28 str. E24377A; Escherichia coli. 23 samples. Type: Expression profiling by array.
Next-generation sequencing measuring mRNA expression in naïve T cells isolated from Mettl3 KO and WT mice
GEO Series GSE100277. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Malignant progression of an ancestral bone marrow clone harboring a CIC-NUTM2A fusion in isolated myeloid sarcoma
GEO Series GSE217874. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
Expression data from Burkholderia multivorans cystic fibrosis clinical isolates
GEO Series GSE28306. Burkholderia multivorans; Burkholderia multivorans ATCC 17616; Burkholderia cenocepacia J2315. 6 samples. Type: Expression profiling by array.
Evolution of biofilm-adapted gene expression profiles in lasR-deficient clinical Pseudomonas aeruginosa isolates
GEO Series GSE191103. Pseudomonas aeruginosa. 36 samples. Type: Expression profiling by high throughput sequencing.
Isolation and in vitro expansion of human colonic stem cells [CGH]
GEO Series GSE31256. Homo sapiens. 4 samples. Type: Genome variation profiling by genome tiling array.
Array CGH of Mexican Patient Isolates and BCS100 Strain
GEO Series GSE7618. Helicobacter pylori. 105 samples. Type: Genome variation profiling by array.
mRNAs in un-injured and injured rat cortical axons isolated after 13 days in culture
GEO Series GSE11730. Rattus norvegicus. 13 samples. Type: Expression profiling by array.
ScienceDex guides
Understand access before you commit
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.