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Dataset results
677 results for “inversion”
Inverse genetics tracing the differentiation pathway of human chondrocytes
GEO Series GSE261806. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Expression differences between inversions and their progenitors in Drosophila melanogaster
GEO Series GSE15565. Drosophila melanogaster. 42 samples. Type: Genome variation profiling by array.
Global chromatin relabeling accompanies spatial inversion of chromatin in rod photoreceptors
GEO Series GSE180006. Mus musculus. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genome-wide expression profiling revealed peripheral effects of CB1 inverse agonists in improving insulin sensitivity and metabolic parameters
GEO Series GSE21069. Mus musculus. 75 samples. Type: Expression profiling by array.
Identification and Validation of Genes with Expression Patterns Inverse to Multiple Metastasis Suppressor Genes in Breast Cancer Cell Lines
GEO Series GSE53668. Homo sapiens. 77 samples. Type: Expression profiling by array.
CRISPR Inversion of CTCF Sites Alters Genome Topology and Enhancer/Promoter Function
GEO Series GSE71275. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Correction of a Factor VIII genomic inversion with designer recombinases
GEO Series GSE159492. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Fig. 3 in Species and shape diversification are inversely correlated among gobies and cardinalfishes (Teleostei: Gobiiformes)
Fig. 3 Graphs of principal component axes for gobiiform families. Points are color-coded by taxon, as depicted in legend at right. APO Apogonidae, BUT Butidae, ELE Eleotridae, GBN Gobionellidae, GOB Gobiidae, KUR Kurtidae, LEI Leiognathidae, ODO Odontobutidae, PEM Pempheridae, PSE Pseudamia. Wireframes describe the shape changes for each principal component, PC1 on the X-axis, with PC2–PC4 on the Y-axis. Light blue wireframe indicates the average specimen shape, and dark blue wireframe shows deformation of 0.1 units on that axis. a PC1 vs. PC2, b PC1 vs. PC3, c PC1 vs. PC
Fig. 7 in Species and shape diversification are inversely correlated among gobies and cardinalfishes (Teleostei: Gobiiformes)
Fig. 7 Environmental preference optimized on the phylogeny. The phylogeny is presented with taxa color-coded as they are in Fig. 3, except taxa in Gobiidae, which are rendered in gray. Branches are colored in accordance with optimized environmental preference: blue freshwater, gray brackish/estuarine water, black saltwater
Fig. 1 in Species and shape diversification are inversely correlated among gobies and cardinalfishes (Teleostei: Gobiiformes)
Fig. 1 Locations of morphometric landmarks. Locations of landmarks used in this study shown on right lateral view of Cheilodipterus isostigmus (USNM 171260). Landmarks digitized are: (1) tip of ascending process of premaxilla, (2) anterior tip of premaxilla, (3) dorsal tip of maxilla, (4) anterior tip of dentary, (5) posteroventral tip of maxilla, (6) posteroventral tip of dentary, (7) dorsal tip of supraoccipital crest, (8) articulation point of first vertebra with basioccipital, (9) dorsal extent of dorsalmost pectoral fin radial, (10) anterior tip of pelvis, (11) anteroventral tip of cleithrum, (12) posterior extent of branchiostegal rays, (13) posterior tip of pelvis and articulation of pelvic fin rays, (14) pterygiophore of first dorsal spine, (15) pterygiophore of last dorsal spine, (16) pterygiophore of first dorsal ray, (17) pterygiophore of last dorsal ray, (18) pterygiophore of first anal spine, (19) pterygiophore of last anal ray, (20) articularion point of last vertebra with hypurals, (21) anterodorsal extent of hypurals
Fig. 6 in Species and shape diversification are inversely correlated among gobies and cardinalfishes (Teleostei: Gobiiformes)
Fig. 6 Results of evolutionary rate-shift analyses in AUTEUR. Inferred shifts in rates of shape change are shown optimized across the gobiiform phylogeny. The four trees represent rate-shift analyses for each major
Improved Detection of Multiple Sclerosis Plaques in Inversion Recovery by Optimization of Sequence Parameters
ClinicalTrials.gov study NCT03108573. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Longterm Outcome of the Delta III Inverse Prosthesis
ClinicalTrials.gov study NCT01873651. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Expression differences between inversions and their progenitors in Drosophila melanogaster embryos
GEO Series GSE21607. Drosophila melanogaster. 4 samples. Type: Genome variation profiling by array.
Not1 and Not4 inversely determine mRNA solubility that sets the dynamics of co-translational events
GEO Series GSE193912. Saccharomyces cerevisiae. 48 samples. Type: Other.
Recovery of phenotypes obtained by adaptive evolution through inverse metabolic engineering
GEO Series GSE36118. Schizosaccharomyces pombe; Saccharomyces cerevisiae. 14 samples. Type: Expression profiling by array.
Regulatory inversion in NAC networks steers the timing of age-dependent cell death in plants
GEO Series GSE92371. Arabidopsis thaliana. 446 samples. Type: Expression profiling by high throughput sequencing; Expression profiling by array.
Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set.
Inverse Modeling Using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting
Smart lighting systems in low energy commercial buildings can be expensive to implement and commission. Studies have also shown that only 50% of these systems are used after installation, and those used are not operated at full capacity due to inadequate commissioning and lack of personalization. Wireless sensor networks (WSN) have great potential to enable personalized smart lighting systems for real-time model predictive control of integrated smart building systems. In this paper we present a framework for using a WSN to develop a real-time indoor lighting inverse model as a piecewise linear function of window and artificial light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days of artificial light data, this model was able to predict the light level at seven monitored workstations with accuracy sufficient for daylight harvesting and lighting control around fixed work surfaces. The reduced order model was also designed to be used for long term evaluation of energy and comfort performance of the predictive control algorithms. This paper describes a WSN experiment from an implementation at the Sustainability Base at NASA Ames, a living laboratory that offers opportunities to test and validate information-centric smart building control systems.
Serial Inversions Induce Tissue-specific Architectural Stripes, Gene Misexpression and Congenital Malformations [RNA-Seq]
GEO Series GSE116793. Mus musculus. 23 samples. Type: Expression profiling by high throughput sequencing.
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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.