6365 results for "11+1双色球多少钱一注"

in 20.45 milliseconds.

• Reference epigenome SMC02_WGBS data generated from KEP study

  A SMC02_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003872

• Reference epigenome SMC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC03_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003847

• Reference epigenome SMC07_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC07_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003851

• Reference epigenome SMC05_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC05_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003849

• Reference epigenome SMC06_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC06_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003850

• Reference epigenome KNIH010 miRNA-seq data generated from KEP study

  A KNIH010 miRNA-seq single end data for podocytes

  Dataset EGAD00001002769

• Reference epigenome Pancreas-Islet06 miRNA-Seq data generated from KEP study

  Pancreas-Islet06 miRNA-Seq single end data

  Dataset EGAD00001007187

• Reference epigenome SMC05_WGBS data generated from KEP study

  A SMC05_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003873

• Reference epigenome SMC03_WGBS data generated from KEP study

  A SMC03_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003888

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

• Reference epigenome SMC04_WGBS data generated from KEP study

  A SMC04_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003889

• Reference epigenome Fat-adipocyte03 miRNA-Seq data generated from KEP study

  Fat-adipocyte03 miRNA-Seq single end data

  Dataset EGAD00001007190

• Reference epigenome Fat-adipocyte04 miRNA-Seq data generated from KEP study

  Fat-adipocyte04 miRNA-Seq single end data

  Dataset EGAD00001007191

• Targeted capture ctDNA Library CRCQV34Run005-14

  Targeted capture ctDNA Library CRCQV34Run005-14 from patient PBC0002816, TNBC FFPE sample

  Dataset EGAD00001010345

• Reference epigenome Fat-adipocyte05 miRNA-Seq data generated from KEP study

  Fat-adipocyte05 miRNA-Seq single end data

  Dataset EGAD00001007192

• input

  Input dataset of human islets. To be used in conjuction with Cohesin and Mediator ChIP-Seq datasets.

  Dataset EGAD00001005209

• Reference epigenome SMC08_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC08_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003852

• Reference epigenome SMC09_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC09_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003853

• HipSci - Monogenic Diabetes - RNA Sequencing - July 2017

  HipSci - Monogenic Diabetes - RNA Sequencing - July 2017

  Dataset EGAD00001003530

• Targeted capture ctDNA Library CRCQV42Run022-6

  Targeted capture ctDNA Library CRCQV42Run022-6 from patient PBC0002406, TNBC FFPE sample

  Dataset EGAD00001010457

• Reference epigenome SMC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC04_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003848

• Targeted capture ctDNA Library CRCQV42Run42-16

  Targeted capture ctDNA Library CRCQV42Run42-16 from patient PBC0001413, TNBC FFPE sample

  Dataset EGAD00001010827

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

• Targeted capture ctDNA Library CRCQV34Run005-5

  Targeted capture ctDNA Library CRCQV34Run005-5 from patient PBC0002062, TNBC FFPE sample

  Dataset EGAD00001010350

• Targeted capture ctDNA Library CRCQV42Run019-21

  Targeted capture ctDNA Library CRCQV42Run019-21 from patient PBC0001310, TNBC FFPE sample

  Dataset EGAD00001010422

• Targeted capture ctDNA Library CRCQV34Run005-10

  Targeted capture ctDNA Library CRCQV34Run005-10 from patient PBC0002459, TNBC FFPE sample

  Dataset EGAD00001010341

• Targeted capture ctDNA Library CRCQV42Run019-6

  Targeted capture ctDNA Library CRCQV42Run019-6 from patient PBC0001295, TNBC FFPE sample

  Dataset EGAD00001010423

• Targeted capture ctDNA Library CRCQV34Run007-24

  Targeted capture ctDNA Library CRCQV34Run007-24 from patient PBC0001051, TNBC FFPE sample

  Dataset EGAD00001010361

• Targeted capture ctDNA Library CRCQV42Run019-8

  Targeted capture ctDNA Library CRCQV42Run019-8 from patient PBC0001304, TNBC FFPE sample

  Dataset EGAD00001010424

• Targeted capture ctDNA Library CRCQV42Run029-7

  Targeted capture ctDNA Library CRCQV42Run029-7 from patient PBC0004173, TNBC FFPE sample

  Dataset EGAD00001010519

• Covacta OLINK QC metrics

  Olink Explore 1536 dataset from serum of patients enrolled in the COVACTA trial. This dataset includes QC warning flag provided by Olink.

  Dataset EGAD00001011167

• Reference epigenome SMC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC02_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003846

• Reference epigenome Pancreas-Islet07 miRNA-Seq data generated from KEP study

  Pancreas-Islet07 miRNA-Seq single end data

  Dataset EGAD00001007188

• Targeted capture ctDNA Library CRCQV42Run022-20

  Targeted capture ctDNA Library CRCQV42Run022-20 from patient PBC0001845, plasma 12 month repeat sample

  Dataset EGAD00001010455

• Reference epigenome SMC06_WGBS data generated from KEP study

  A SMC06_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003874

• Reference epigenome Islet-derived_MSC08 miRNA-Seq data generated from KEP study

  Islet-derived_MSC08 miRNA-Seq single end data

  Dataset EGAD00001007882

• Reference epigenome Islet-derived_iPSC04 miRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 miRNA-Seq single end data

  Dataset EGAD00001007883

• Reference epigenome SMC07_WGBS data generated from KEP study

  A SMC07_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003875

• oxBS-seq for APL

  Oxidative bisulfite sequencing (oxBS-Seq) for APL

  Dataset EGAD00001008135

• WGBS for APL

  Whole genome bisulfite sequencing (WGBS) for APL

  Dataset EGAD00001008136

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Targeted capture ctDNA Library CRCQV34Run005-9

  Targeted capture ctDNA Library CRCQV34Run005-9 from patient PBC0002429, TNBC FFPE sample

  Dataset EGAD00001010352

• Targeted capture ctDNA Library CRCQV34Run005-12

  Targeted capture ctDNA Library CRCQV34Run005-12 from patient PBC0001224, TNBC FFPE sample

  Dataset EGAD00001010343

• Targeted capture ctDNA Library CRCQV34Run005-8

  Targeted capture ctDNA Library CRCQV34Run005-8 from patient PBC0002108, TNBC FFPE sample

  Dataset EGAD00001010351

• Targeted capture ctDNA Library CRCQV40Run027-22

  Targeted capture ctDNA Library CRCQV40Run027-22 from patient PBC0005116, TNBC FFPE sample

  Dataset EGAD00001010394

• Targeted capture ctDNA Library CRCQV40Run027-20

  Targeted capture ctDNA Library CRCQV40Run027-20 from patient PBC0004565, TNBC FFPE sample

  Dataset EGAD00001010393

• Targeted capture ctDNA Library CRCQV42Run020-5

  Targeted capture ctDNA Library CRCQV42Run020-5 from patient PBC0001315, TNBC FFPE sample

  Dataset EGAD00001010431

• Targeted capture ctDNA Library CRCQV42Run021-12

  Targeted capture ctDNA Library CRCQV42Run021-12 from patient PBC0001653, TNBC FFPE sample

  Dataset EGAD00001010435

• Targeted capture ctDNA Library CRCQV42Run022-19

  Targeted capture ctDNA Library CRCQV42Run022-19 from patient PBC0002680, plasma 6 month repeat sample

  Dataset EGAD00001010454

• Targeted capture ctDNA Library CRCQV42Run024-13

  Targeted capture ctDNA Library CRCQV42Run024-13 from patient PBC0001306, TNBC FFPE sample

  Dataset EGAD00001010466

• Targeted capture ctDNA Library CRCQV42Run026-16

  Targeted capture ctDNA Library CRCQV42Run026-16 from patient PBC0005064, TNBC FFPE sample

  Dataset EGAD00001010489

• Targeted capture ctDNA Library CRCQV42Run42-13

  Targeted capture ctDNA Library CRCQV42Run42-13 from patient PBC0001467, TNBC FFPE sample

  Dataset EGAD00001010825

• Reference epigenome Pancreas-Islet08 miRNA-Seq data generated from KEP study

  Pancreas-Islet08 miRNA-Seq single end data

  Dataset EGAD00001007189

• Targeted capture ctDNA Library CRCQV42Run021-4

  Targeted capture ctDNA Library CRCQV42Run021-4 from patient PBC0001328, TNBC FFPE sample

  Dataset EGAD00001010446

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• Reference epigenome Islet-derived_MSC08 mRNA-Seq data generated from KEP study

  Islet-derived_MSC08 mRNA-Seq paired end data

  Dataset EGAD00001007879

• Reference epigenome Islet-derived_iPSC04 mRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 mRNA-Seq paired end data

  Dataset EGAD00001007880

• Reference epigenome Islet-derived_MSC06 miRNA-Seq data generated from KEP study

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Targeted capture ctDNA Library CRCQV34Run015-20

  Targeted capture ctDNA Library CRCQV34Run015-20 from patient PBC0001432, TNBC FFPE sample

  Dataset EGAD00001010382

• Targeted capture ctDNA Library CRCQV34Run015-22

  Targeted capture ctDNA Library CRCQV34Run015-22 from patient PBC0001673, TNBC FFPE sample

  Dataset EGAD00001010383

• 20180208_EGA_Trench_MetCellLine.1

  Illumina platform sequencing data of SureSelect exome libraries prepared from 3 samples from one donor: a normal, primary breast cancer, and cell line derived from metastasis

  Dataset EGAD00001003956

• Reference epigenome SMC08_WGBS data generated from KEP study

  A SMC08_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003876

• Reference epigenome SMC09_WGBS data generated from KEP study

  A SMC09_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003877

• Targeted capture ctDNA Library CRCQV34Run005-15

  Targeted capture ctDNA Library CRCQV34Run005-15 from patient PBC0002824, TNBC FFPE sample

  Dataset EGAD00001010346

• Targeted capture ctDNA Library CRCQV34Run005-20

  Targeted capture ctDNA Library CRCQV34Run005-20 from patient PBC0002680, TNBC FFPE sample

  Dataset EGAD00001010348

• Targeted capture ctDNA Library CRCQV34Run005-4

  Targeted capture ctDNA Library CRCQV34Run005-4 from patient PBC0001627, TNBC FFPE sample

  Dataset EGAD00001010349

• Targeted capture ctDNA Library CRCQV34Run005-18

  Targeted capture ctDNA Library CRCQV34Run005-18 from patient PBC0001845, TNBC FFPE sample

  Dataset EGAD00001010347

• Targeted capture ctDNA Library CRCQV34Run015-23

  Targeted capture ctDNA Library CRCQV34Run015-23 from patient PBC0001255, TNBC FFPE sample

  Dataset EGAD00001010384

• Targeted capture ctDNA Library CRCQV34Run005-13

  Targeted capture ctDNA Library CRCQV34Run005-13 from patient PBC0002383, TNBC FFPE sample

  Dataset EGAD00001010344

• Targeted capture ctDNA Library CRCQV40Run027-17

  Targeted capture ctDNA Library CRCQV40Run027-17 from patient PBC0004076, TNBC FFPE sample

  Dataset EGAD00001010390

• Targeted capture ctDNA Library CRCQV34Run015-24

  Targeted capture ctDNA Library CRCQV34Run015-24 from patient PBC0002255, TNBC FFPE sample

  Dataset EGAD00001010385

• Targeted capture ctDNA Library CRCQV40Run027-18

  Targeted capture ctDNA Library CRCQV40Run027-18 from patient PBC0004350, TNBC FFPE sample

  Dataset EGAD00001010391

• Targeted capture ctDNA Library CRCQV40Run027-19

  Targeted capture ctDNA Library CRCQV40Run027-19 from patient PBC0004414, TNBC FFPE sample

  Dataset EGAD00001010392

• Targeted capture ctDNA Library CRCQV42Run017-24

  Targeted capture ctDNA Library CRCQV42Run017-24 from patient PBC0002294, TNBC FFPE sample

  Dataset EGAD00001010414

• Targeted capture ctDNA Library CRCQV42Run020-4

  Targeted capture ctDNA Library CRCQV42Run020-4 from patient PBC0001312, TNBC FFPE sample

  Dataset EGAD00001010430

• Targeted capture ctDNA Library CRCQV42Run021-10

  Targeted capture ctDNA Library CRCQV42Run021-10 from patient PBC0001470, TNBC FFPE sample

  Dataset EGAD00001010433

• Targeted capture ctDNA Library CRCQV42Run021-9

  Targeted capture ctDNA Library CRCQV42Run021-9 from patient PBC0001323, TNBC FFPE sample

  Dataset EGAD00001010449

• Targeted capture ctDNA Library CRCQV42Run022-8

  Targeted capture ctDNA Library CRCQV42Run022-8 from patient PBC0002826, TNBC FFPE sample

  Dataset EGAD00001010459

• Targeted capture ctDNA Library CRCQV42Run023-19

  Targeted capture ctDNA Library CRCQV42Run023-19 from patient PBC0002383, plasma baseline repeat sample

  Dataset EGAD00001010462

• Targeted capture ctDNA Library CRCQV42Run023-20

  Targeted capture ctDNA Library CRCQV42Run023-20 from patient PBC0001224, plasma baseline repeat sample

  Dataset EGAD00001010463

• Targeted capture ctDNA Library CRCQV42Run024-14

  Targeted capture ctDNA Library CRCQV42Run024-14 from patient PBC0002853, TNBC FFPE sample

  Dataset EGAD00001010467

• Targeted capture ctDNA Library CRCQV42Run025-19

  Targeted capture ctDNA Library CRCQV42Run025-19 from patient PBC0003364, TNBC FFPE sample

  Dataset EGAD00001010482

• Targeted capture ctDNA Library CRCQV42Run025-5

  Targeted capture ctDNA Library CRCQV42Run025-5 from patient PBC0001589, TNBC FFPE sample

  Dataset EGAD00001010483

• Targeted capture ctDNA Library CRCQV42Run025-7

  Targeted capture ctDNA Library CRCQV42Run025-7 from patient PBC0003334, TNBC FFPE sample

  Dataset EGAD00001010485

• Reference epigenome SMC01_WGBS data generated from KEP study

  A SMC01_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003871

• Targeted capture ctDNA Library CRCQV42Run034-24

  Targeted capture ctDNA Library CRCQV42Run034-24 from patient PBC0001396, TNBC FFPE sample

  Dataset EGAD00001010572

• Targeted capture ctDNA Library CRCQV42Run036-10

  Targeted capture ctDNA Library CRCQV42Run036-10 from patient PBC0002062, plasma 21 month sample

  Dataset EGAD00001010592

• Targeted capture ctDNA Library CRCQV42Run036-20

  Targeted capture ctDNA Library CRCQV42Run036-20 from patient PBC0003364, plasma 15 month sample

  Dataset EGAD00001010599

• Targeted capture ctDNA Library CRCQV42Run041-8

  Targeted capture ctDNA Library CRCQV42Run041-8 from patient PBC0001404, TNBC FFPE sample

  Dataset EGAD00001010638

• Targeted capture ctDNA Library CRCQV42Run42-14

  Targeted capture ctDNA Library CRCQV42Run42-14 from patient PBC0001375, TNBC FFPE sample

  Dataset EGAD00001010826

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• HipSci - Monogenic Diabetes - RNA Sequencing - October 2016

  HipSci - Monogenic Diabetes - RNA Sequencing - October 2016

  Dataset EGAD00001003180

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• SF12374

  SF12374 snATAC Oligodendroglioma. Tumor Location: Right frontal. Age: 33. Sex: Male.

  Dataset EGAD00001006315

• Methylation tumour profiles for EGAS00001004572

  Bulk methylation tumour profiles from infinium methylation epic bead kit in EGAS00001004572

  Dataset EGAD00001006909

• Targeted capture ctDNA Library CRCQV42Run022-22

  Targeted capture ctDNA Library CRCQV42Run022-22 from patient PBC0002062, plasma 12 month repeat sample

  Dataset EGAD00001010456

• Targeted capture ctDNA Library CRCQV42Run035-20

  Targeted capture ctDNA Library CRCQV42Run035-20 from patient PBC0003334, plasma 6 month sample

  Dataset EGAD00001010585

• GBM-Space: Joint Transcriptome and Chromatin Accessibility Profiling of Glioblastoma (10x Genomics - Multiome)

  GBM-Space: Joint Transcriptome and Chromatin Accessibility Profiling of Glioblastoma (10x Genomics - Multiome)

  Dataset EGAD00001015526

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002