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3,690 results for “Computing”
Computer Guided Versus Conventional Surgical Removal of Deeply Impacted Mandibular Third Molar With Preservation of External Oblique Ridge
ClinicalTrials.gov study NCT03447067. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Connecting Transcriptomics with Computational Modeling to Reveal Developmental Adaptations in Pediatric Human Atrial Tissue
GEO Series GSE276839. Homo sapiens. 117 samples. Type: Expression profiling by array.
A Reproducibility-Based Computational Framework Identifies An Inducible, Enhanced Antiviral Dendritic Cell State In HIV-1 Elite Controllers
GEO Series GSE108445. Homo sapiens. 420 samples. Type: Expression profiling by high throughput sequencing.
Computationally resolved neuroprogenitor cell biomarkers associate with human disorders
GEO Series GSE303501. Mus musculus. 10 samples. Type: Expression profiling by array.
Computational Repurposing of Bumetanide for Preventing or Treating Alzheimer’s Disease
GEO Series GSE182765. Mus musculus; Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Comparative Computational RNA Analysis of Cardiac-Derived Progenitor Cells and Their Extracellular Vesicles
GEO Series GSE196556. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Enhancers of host immune tolerance to bacterial infection discovered using linked computational and experimental approaches II
GEO Series GSE196424. Xenopus laevis. 27 samples. Type: Expression profiling by array.
SnowEx20 Grand Mesa IOP Computed Tomography Snow Microstructure V001
This data set characterizes snow microstructure for 6 snow pits from the SnowEx 2020 Grand Mesa Intensive Observation Period (February 2020) using microcomputed tomography (micro-CT). Included with this data set are two- and three-dimensional microstructural analysis of DMP/DEP casted and un-casted snow samples, available as .xlsx and .txt file, and visual representations of the three-dimensional snow structure, available is .bmp image files. Containers of snow were collected at 6 snow pits in approximately 17 cm intervals. There were approximately 5-8 discrete containers per pit and each container had an ~2 cm overlap with the sample below. A 6-cm section of snow was dissected from each container of snow, which were analyzed in ~2 cm sub-sections. Each sub-section was scanned in three intervals using a micro-CT instrument. The three interval scans comprise multiple slices, and were combined into the reconstructed final scan used for calculating the snow microstructural data.
37-Day microgravity exposure in 16-Week female C57BL/6J mice during the NASA Rodent Research 1 mission is associated with bone loss specific to weight-bearing skeletal sites (femur and vertebrae, micro computed tomography)
Exposure to weightlessness in microgravity and elevated space radiation are associated with rapid bone loss in mammals, but questions remain about their mechanisms of action and relative importance. In this study, we tested the hypothesis that bone loss during spaceflight in Low Earth Orbit is primarily associated with site-specific microgravity unloading of weight-bearing sites in the skeleton. Microcomputed tomography and histological analyses of bones from mice space flown on ISS for 37 days in the NASA Rodent Research-1 experiment show significant site-specific cancellous and cortical bone loss occurring in the femur, but not in L2 vertebrae. The lack of bone degenerative effects in the spine in combination with same-animal paired losses in the femur suggests that space radiation levels in Low Earth Orbit or other systemic stresses are not likely to significantly contribute to the observed bone loss. Remarkably, spaceflight is also associated with accelerated progression of femoral head endochondral ossification. This suggests the microgravity environment promotes premature progression of secondary ossification during late stages of skeletal maturation at 21 weeks. Furthermore, mice housed in the NASA ISS Rodent Habitat during 1g ground controls maintained or gained bone relative to mice housed in standard vivarium cages that showed significant bone mass declines. These findings suggest that housing in the Rodent Habitat with greater topological enrichment from 3D wire-mesh surfaces may promote increased mechanical loading of weight-bearing bones and maintenance of bone mass. In summary, our results indicate that in female mice approaching skeletal maturity, mechanical unloading of weight-bearing sites is the major cause of bone loss in microgravity, while sites loaded predominantly by muscle activity, such as the spine, appear unaffected. Additionally, we identified early-onset of femoral head epiphyseal plate secondary ossification as a novel spaceflight skeletal unloading effect that may lead to premature long bone growth arrest in microgravity. This study derives results from femur and vertebrae using the micro computed tomography assay.
Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform
Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the esti- mated current state distribution of a component and ex- pected profiles of future usage. In general, this requires simulations of the component using the underlying mod- els. In this paper, we develop a simulation-based pre- diction methodology that achieves computational effi- ciency by performing only the minimal number of sim- ulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented trans- form, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved com- putational efficiency without sacrificing prediction accu- racy.
Health Monitoring and Prognostics for Computer Servers
**Abstract** Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. **Bios** Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.
A Local Distributed Peer-to-Peer Algorithm Using Multi-Party Optimization Based Privacy Preservation for Data Mining Primitive Computation
This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and therefore, is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacypreserving clustering, frequent itemset mining, and statistical aggregate computation.
Experimental and Computational Insights into the Mechanism that CHRONO Regulates the Circadian Clock
GEO Series GSE237793. Homo sapiens. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Temporal Splicing Switches in Elements of the TNF-Pathway Identified by Computational Analysis of Transcriptome Data for Human Cell Lines
GEO Series GSE162602. Homo sapiens. 33 samples. Type: Expression profiling by high throughput sequencing.
A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion [Bulk RNA-seq]
GEO Series GSE162908. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Computational map of colon polyps
GEO Series GSE223918. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
The role of structural vs cellular remodeling in arrhythmogenesis: personalized computer models of atrial fibrillation
GEO Series GSE269882. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion
GEO Series GSE162910. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Computational guideline for small RNA profiling in virus-infected plants
GEO Series GSE104799. Arabidopsis thaliana. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
A harmonized atlas of spinal cord cell types and their computational classification
GEO Series GSE158380. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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.