6376 results for "11+1双色球多少钱一注"

in 11.17 milliseconds.

• Targeted capture ctDNA Library CRCQV42Run049-22

  Targeted capture ctDNA Library CRCQV42Run049-22 from patient PBC0002481, buffy coat sample

  Dataset EGAD00001010775

• Targeted capture ctDNA Library CRCQV42Run049-23

  Targeted capture ctDNA Library CRCQV42Run049-23 from patient PBC0001409, buffy coat sample

  Dataset EGAD00001010776

• Targeted capture ctDNA Library CRCQV42Run049-4

  Targeted capture ctDNA Library CRCQV42Run049-4 from patient PBC0004537, buffy coat sample

  Dataset EGAD00001010777

• Targeted capture ctDNA Library CRCQV42Run050-19

  Targeted capture ctDNA Library CRCQV42Run050-19 from patient PBC0001527, buffy coat sample

  Dataset EGAD00001010792

• Targeted capture ctDNA Library CRCQV42Run051-19

  Targeted capture ctDNA Library CRCQV42Run051-19 from patient PBC0002294, buffy coat sample

  Dataset EGAD00001010812

• SNF_RNAseq_CD138n_20f

  TPM matrices of counts from RNA sequencing (RNAseq) from baseline CD138(–) BM fractions.

  Dataset EGAD00001011142

• 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

• Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Dataset EGAD00001000081

• APP-G-1

  Array data from 4778 individuals from general population of rural Uganda

  Dataset EGAD00010000965

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008038

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008048

• Average_5hmC_DIFF_Roadmap

  Average hydroxymethylation difference 12 months vs 0 months at Roadmap Epigenomics chromatin state annotations from different cell types. Data from 8 individuals.

  Dataset EGAD00010002415

• Sequencing data for oesophageal and related samples - OACs release 1 (RNA)

  Dataset EGAD00001002259

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008058

• WXS_TALL_t_14_16_translocation

  Whole exome-sequencing for patients with t(14;16) translocated T-ALL.

  Dataset EGAD50000001783

• CSC DDR dataset

  CSC DDR dataset contains 4 bam files of two pairs of colorectal CSCs sensitive and resistant to ATR or CHK1 inhibitor

  Dataset EGAD00001006774

• Targeted capture ctDNA Library CRCQV42Run017-8

  Targeted capture ctDNA Library CRCQV42Run017-8 from patient PBC0001224, plasma 15 month sample

  Dataset EGAD00001010415

• Targeted capture ctDNA Library CRCQV42Run035-24

  Targeted capture ctDNA Library CRCQV42Run035-24 from patient PBC0004565, plasma 9 month sample

  Dataset EGAD00001010588

• Targeted capture ctDNA Library CRCQV42Run045-12

  Targeted capture ctDNA Library CRCQV42Run045-12 from patient PBC0001068, plasma 3 month sample

  Dataset EGAD00001010683

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Targeted capture ctDNA Library CRCQV42Run024-9

  Targeted capture ctDNA Library CRCQV42Run024-9 from patient PBC0003641, plasma baseline sample

  Dataset EGAD00001010475

• Targeted capture ctDNA Library CRCQV42Run032-8

  Targeted capture ctDNA Library CRCQV42Run032-8 from patient PBC0001589, buffy coat sample

  Dataset EGAD00001010547

• Targeted capture ctDNA Library CRCQV42Run039-21

  Targeted capture ctDNA Library CRCQV42Run039-21 from patient PBC0003014, plasma 3 month sample

  Dataset EGAD00001010616

• Targeted capture ctDNA Library CRCQV42Run044-18

  Targeted capture ctDNA Library CRCQV42Run044-18 from patient PBC0003376, plasma 12 month sample

  Dataset EGAD00001010668

• Targeted capture ctDNA Library CRCQV42Run046-20

  Targeted capture ctDNA Library CRCQV42Run046-20 from patient PBC0001176, plasma 3 month sample

  Dataset EGAD00001010711