Platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels that includes two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: * The open public image database, here, providing in situ hybridization data mapping gene expression across the anatomic structures inherent in glioblastoma, as well as associated histological data suitable for neuropathological examination * A companion database (Ivy GAP Clinical and Genomic Database) offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access. The hope is that researchers all over the world will mine these data and identify trends, correlations, and interesting leads for further studies with significant translational and clinical outcomes. The Ivy Glioblastoma Atlas Project is a collaborative partnership between the Ben and Catherine Ivy Foundation, the Allen Institute for Brain Science and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment.

The Ivy Glioblastoma Atlas Project (Ivy GAP) is a detailed anatomically based transcriptomic atlas of human glioblastoma tumors. As collaborators, the Ivy Foundation funded the Allen Institute and the Swedish Neuroscience Institute to design and create the atlas. The Paul G. Allen Family Foundation also supported the project. This resource consists of a viewer interface that resolves the manually- and machine-annotated histologic images (H&E and RNA in situ hybridization) at 0.5 µm/pixel, a transcriptome browser to view and mine the anatomically-based RNA-Seq samples, an application programming interface, help documentation that describes the methods and how to use the resource, as well as SNP array data and the supporting longitudinal clinical information and MRI time course data. The resource is made available to the public without charge as part of the Ivy GAP (http://glioblastoma.alleninstitute.org/) via the Allen Institute data portal (http://www.brain-map.org), the Ivy GAP Clinical and Genomic Database (http://ivygap.org/) via the Swedish Neuroscience Institute (http://www.swedish.org/services/neuroscience-institute), and The Cancer Imaging Archive (https://wiki.cancerimagingarchive.net/display/Public/Ivy+GAP). The Ivy GAP processed data at GEO includes normalized RNA-Seq FPKM files used for analysis in "An anatomic transcriptional atlas of glioblastoma,” which is under review. Other processed data files as well as sample and donor meta-data and QC metrics are available at http://glioblastoma.alleninstitute.org/static/download.html. The raw RNA-Seq and SNP array data will be submitted to dbGaP.

Abstract:

The Ivy Glioblastoma Atlas Project represents a fundamental tool for investigating the cellular and molecular underpinnings of glioblastoma. It offers an accessible online atlas and database containing valuable clinical and genomic information, which will undoubtedly facilitate future studies on glioblastoma pathogenesis, diagnosis, and therapeutic approaches. Glioblastoma is a highly aggressive brain tumor with a bleak prognosis, and its intricate molecular and cellular characteristics have not been fully elucidated in relation to conventional diagnostic histologic features. The dataset provided is comprised of de-identified clinical data pertaining to both patients and tumors.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Acknowledgments:

Puchalski RB, Shah N, Miller J, et al. An anatomic transcriptional atlas of human glioblastoma. Science. 2018;360(6389):660-663. doi:10.1126/science.aaf2666

U-BRITE last update: 07/28/2023

This dataset comprises two paired sets of expert segmentation labels for tumor sub-compartments of the pre-operative multi-institutional scans of the Ivy Glioblastoma Atlas Project (Ivy GAP) collection of The Cancer Imaging Archive (TCIA). These labels have been approved by independent expert board-certified neuroradiologists at the Hospital of the University of Pennsylvania and at Case Western Reserve University. Furthermore, for each of the paired sets of approved labels, a diverse comprehensive panel of radiomic features is provided, along with their corresponding skull-stripped and co-registered multi-parametric magnetic resonance imaging (mpMRI) volumes (i.e. native (T1) and post-contrast T1-weighted (T1-Gd), T2, T2-FLAIR), in NIfTI format. The pre-operative mpMRI scans were identified in the Ivy GAP collection via radiological assessment. These scans were initially skull-stripped and co-registered to a common anatomical atlas (provided within this dataset), before their tumor segmentation labels were produced following a consistent annotation protocol across the two institutions. The final labels were used to extract a rich panel of radiomic features through the Cancer Imaging Phenomics Toolkit (CaPTk), comprising intensity, volumetric, morphologic, histogram-based, and textural parameters compliant with the Image Biomarker Standardisation Initiative (IBSI), as well as through a 3D Slicer extension for the novel CoLlAGe feature family. Radiomic features robust to variability in segmentations were then identified following a statistical robustness analysis. The approved expert segmentation labels should enable quantitative computational and clinical studies without the need to repeat manual annotations, whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational competitions, such as the http://braintumorsegmentation.org/" rel="nofollow">International Brain Tumor Segmentation (BraTS) challenge. The provided panel of robust radiomic features may facilitate research integrative of the molecular characterization offered by the Allen Institute, and hence allow associations with molecular markers (radiogenomics), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. The complete reproducibility analysis can be found in the associated publication citation found in the “Citations & Data Usage Policy”. Specifically, the released data comprises of 1) the available expert segmentation labels of the various tumor sub-compartments performed at each institution (i.e. 34 subjects segmented at UPenn, 34 subjects segmented at CWRU), with a total of 37 subjects (including 31 paired segmentations performed at both UPenn and CWRU), in the original space they were created (i.e., SRI for UPenn and MNI for CWRU), with 2) their corresponding co-registered and skull-stripped structural mpMRI scans (i.e., in SRI for UPenn and in MNI for CWRU), 3) the paired expert segmentation labels that were available for the 31 subjects, all being co-registered in the SRI atlas, 4) the corresponding SRI and MNI anatomical atlas files that we employed, 5) the complete set of 11,700 extracted radiomic features per subject, for each of the 31 included subjects, 6) the metadata relating to the metrics we utilized for the evaluation of the inter-rater agreement, as well as 7) the parameters used for the radiomic feature extraction and the correlation analysis results for identifying robust radiomic features, for the 28 subjects, and finally 8) the specific identified robust/reproducible radiomic features. All image related files are provided in NIfTI format, while the metadata files are provided in tabular formats (.xlsx and .csv). MNI atlas: see (Montreal Neurological Institute, https://mcin.ca/research/neuroimaging-methods/atlases/" rel="nofollow">https://mcin.ca/research/neuroimaging-methods/atlases/ ) SRI atlas: see (T. Rohlfing, et al. (2010) DOI: https://doi.org/10.1002/hbm.20906" rel="nofollow">10.1002/hbm.20906 , https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2915788/" rel="nofollow">PMC2915788)

This dataset contains the ONCOhabitats processing results for the patients with complete pre-surgical MRI (T1, T1-Gd, T2, FLAIR and DSC perfusion) included at the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.

The ONCOhabitats platform includes two main services:

1. The glioblastoma (GBM) segmentation service implements the MRI preprocessing and GBM morphological segmentation modules.

Preprocessing: Several artefacts are corrected in this module such as magnetic bias field inhomogeneities, noise or spike artifacts. Additionally, automated registration, brain extraction and intensity normalization are conducted to generate a consistent multi-parametric high quality MRI of the brain.

Segmentation: This module implements a state of the art 3D Convolutional Neural Network (CNN) classifier based on a U-Net architecture to delineate the tumor tissues.

1. The GBM Hemodynamic Tissue Signature service implements the MRI preprocessing, GBM morphological segmentation, DSC quantification and the Hemodynamic Tissue Signature modules.

Preprocessing: Several artefacts are corrected in this module such as magnetic bias field inhomogeneities, noise or spike artifacts. Additionally, automated registration, brain extraction and intensity normalization are conducted to generate a consistent multi-parametric high quality MRI of the brain.

Segmentation: This module implements a state of the art 3D Convolutional Neural Network (CNN) classifier based on a U-Net architecture to delineate the tumor tissues.

DSC Perfussion Quantification: This module quantifies the hemodynamic indices derived from of the Dynamic Susceptibility Contrast perfusion sequence. Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF), Mean Transit Time (MTT) are computed.

Hemodynamic Tissue Signature: Hemodynamic MTS provides an automated unsupervised method to describe the heterogeneity of the enhancing tumor and edema tissues, in terms of the angiogenic process located at these regions. We consider 4 sub-compartments for the GBM, closely related to the more angiogenic enhancing tumor part, the less angiogenic enhancing tumor area, the potentially tumour infilatrated edema and the pure vasogenic edema.

For each patient, we include a PDF report containing an analysis summary; two folders with the resulting images in MNI and native spaces; and a third folder with the transformation matrices.

*Users of this data results should include references to the following citations:

1. Juan-Albarracín, J., Fuster-Garcia, E., Pérez-Girbés, A., Aparici-Robles, F., Alberich-Bayarri, Á., Revert-Ventura, A., ... & García-Gómez, J. M. (2018). Glioblastoma: vascular habitats detected at preoperative dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging predict survival. Radiology, 287(3), 944-954.

2. Álvarez‐Torres, M., Juan‐Albarracín, J., Fuster‐Garcia, E., Bellvís‐Bataller, F., Lorente, D., Reynés, G., ... & García‐Gómez, J. M. (2020). Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. Journal of Magnetic Resonance Imaging, 51(5), 1478-1486.

The original data was presented in:

Shah, N., Feng, X., Lankerovich, M., Puchalski, R. B., & Keogh, B. (2016). Data from Ivy Glioblastoma Atlas Project (IvyGAP) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.XLwaN6nL

Puchalski RB, Shah N, Miller J, Dalley R, Nomura SR, Yoon J-G, Smith KA, Lankerovich M, Bertagnolli D, Bickley K, Boe AF, Brouner K, Butler S, Caldejon S, Chapin M, Datta S, Dee N, Desta T, Dolbeare T, Dotson N, Ebbert A, Feng D, Feng X, Fisher M, Gee G, Goldy J, Gourley L, Gregor BW, Gu G, Hejazinia N, Hohmann J, Hothi P, Howard R, Joines K, Kriedberg A, Kuan L, Lau C, Lee F, Lee H, Lemon T, Long F, Mastan N, Mott E, Murthy C, Ngo K, Olson E, Reding M, Riley Z, Rosen D, Sandman D, Shapovalova N, Slaughterbeck CR, Sodt A, Stockdale G, Szafer A, Wakeman W, Wohnoutka PE, White SJ, Marsh D, Rostomily RC, Ng L, Dang C, Jones A, Keogh B, Gittleman HR, Barnholtz-Sloan JS, Cimino PJ, Uppin MS, Keene CD, Farrokhi FR, Lathia JD, Berens ME, Iavarone A, Bernard A, Lein E, Phillips JW, Rostad SW, Cobbs C, Hawrylycz MJ, Foltz GD. (2018). An anatomic transcriptional atlas of human glioblastoma. Science, 360(6389), 660–663. https://doi.org/10.1126/science.aaf2666

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

4564 Global exporters importers export import shipment records of Ivy extract with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Solution Publishing by Allforce Ivy League Business Pros (ILBP) Elite Ivy League Graduate Database for Precision Networking Solution Publishing by Allforce offers a premium database connecting you to over 150,000 Ivy League alumni. This exclusive dataset enables targeted outreach to graduates from Harvard, Yale, Princeton, Columbia, Brown, Dartmouth, UPenn, and Cornell. Core Dataset Features

Comprehensive Alumni Coverage: Direct access to 150,000+ verified Ivy League graduates Detailed Educational Profiles: Information on degrees, graduation years, and specialized programs Advanced Segmentation Options: Filter by industry, job function, and seniority level Regular Data Verification: Continuous updates ensure data accuracy and compliance

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Solution Publishing by Allforce Ivy League Business Pros provides unmatched access to this elite professional network, enabling sophisticated marketing and recruitment strategies targeting this influential demographic.RetryClaude can make mistakes. Please double-check responses.

772 Global exporters importers export import shipment records of Ivy leaf extract with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

316 Global import shipment records of Ivy Gourd with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

We present two databases of phenolic content measurements of three ivy species (Hedera L., Araliaceae) that are naturally distributed across continental Europe: H. helix, H. hibernica, and H. iberica. We sampled a total of 82 ivy populations in the wild in the Iberian Peninsula representing adequately the natural distribution of the species in the sampled area and the global distribution of H. iberica, endemic of the south-west of the Iberian Peninsula. In each population five individuals were sampled whenever it was possible and a leaf from a vegetative branch (VL) and another from a reproductive branch (RL) were collected for phenolic measurement. Geographic information (latitude, longitude and altitude) was retrieved for each population. Phenolic content was estimated as the absorbance at 329 nm per mg of fresh leaf weight (A₃₂₉), as the absorption peak of phenylpropanoids, the most abundant phenolic compounds of ivies, is located at this wavelength (Murray & Hackett, 1991). As an additional estimate we measured the area under the absorbance curve for the interval 280-400 nm per mg of fresh leaf weight (AUC_280-400) following Del-Castillo-Alonso et al. (2015). Climatic information for each locality was retrieved for 22 macroclimatic variables available in WorldClim 2.1 with a 2.5min resolution, including 19 bioclimatic variables, solar radiation, water vapor pressure and wind speed (Fick and Hijmans, 2017). The first version of the presented databases (v1) includes all the geographic, phenolic and climatic information used for the analyses in Gallego-Narbón et al. (under review). All the samples collected are available at the herbarium of Universidad Autónoma de Madrid (MAUAM).

• The first database (Hedera_Phenolics_samples_v1.xlsx) includes sample information (species, population, individual number, leaf type and sample id), geographic information (latitude, longitude and altitude) and phenolic content measurements (A₃₂₉ and AUC_280-400) for 748 leaf samples of 82 ivy populations of H. helix, H. hibernica and H. iberica, This information is included in the first sheet (SamplePhen). The second sheet includes an explanation of the variables of the first sheet (SampleVar).
• The second database (Hedera_Phenolics_populations_v1.xlsx) includes population information (species, population, individual number, leaf type and sample id), geographic information (latitude, longitude and altitude) and mean phenolic content measurements (A₃₂₉ and AUC_280-400) per population for 82 ivy populations of H. helix, H. hibernica and H. iberica, and climatic information for the 22 macroclimatic variables extracted from WorldClim 2.1 per population. This information is included in the first sheet (PopulationPhen). The second sheet includes an explanation of the variables of the first sheet (PopulationVar).

This dataset provides information about the number of properties, residents, and average property values for Ivy Boulevard cross streets in Babbitt, MN.

1136 Global exporters importers export import shipment records of Ivy leaves with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

12334 Global exporters importers export import shipment records of Ivy gourd with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Dataset of M3 Wood & Ivy 8-key alto fagotto containing basic data.

This dataset provides information about the number of properties, residents, and average property values for Ivy Drive cross streets in Merrimack, NH.

This dataset provides information about the number of properties, residents, and average property values for Ivy Court cross streets in Orange, NJ.

This dataset provides information about the number of properties, residents, and average property values for Ivy Avenue cross streets in Henrico, VA.

This dataset provides information about the number of properties, residents, and average property values for Ivy Branch Drive cross streets in Blue Ridge, VA.

This dataset provides information about the number of properties, residents, and average property values for Ivy Log Drive cross streets in Austell, GA.