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2,107 results for “margin;”
TCP5 controls leaf margin development by regulating the KNOX and BEL-like transcription factors in Arabidopsis
GEO Series GSE135305. Arabidopsis thaliana. 6 samples. Type: Expression profiling by high throughput sequencing.
Glioblastoma cell fate is differentially regulated by the microenvironments of the tumour bulk and infiltrative margin.
GEO Series GSE174470. Mus musculus. 3162 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq analysis of follicular (FO) and marginal zone (MZ) B cells, isolated from TLR9WT or TLR9-/- BALB/c mice, stimulated with TLR7 agonists (CL097, Invivogen) or left unstimulated .
GEO Series GSE202105. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq analysis of follicular (FO), marginal zone (MZ) and aged-associated B cells (ABC) across three TLR9 genotypes (TLR9WT, TLR9-/- and non-signaling TLR9P915H/P915H) isolated from a mixed chimeric
GEO Series GSE181283. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
Mutational landscape of ocular marginal zone lymphoma identified frequent alterations of TNFAIP3 followed by TBL1XR1 and CREBBP mutation
GEO Series GSE84126. Homo sapiens. 5 samples. Type: Expression profiling by array.
Genomic profiling of splenic marginal zone lymphoma (SMZL)
GEO Series GSE34768. Homo sapiens. 53 samples. Type: Genome variation profiling by array.
Data associated with 'Processes at the margins of supraglacial debris cover: quantifying dirty ice ablation and debris redistribution'
<p>Data associated with Fyffe, C. L., Woodget, A. S., Kirkbride, M. P., Deline, P., Westoby, M. J. and Brock, B. W. (2020) Processes at the margins of supraglacial debris cover: quantifying dirty ice ablation and debris redistribution, Earth Surface Processes and Landforms, doi:10.1002/esp.4879.</p> <p>The file All_quadrat_data_Z contains the data associated within each of the quadrats which were positioned on the ice surface. Some of the values are extracted from the UAV derived data within the area of the quadrat, while other data is based on measurements from the ground truth images (taken at ground level). The main data types are given below. The data sub-types and units are given in the file header.</p> <ul> <li>Quadrat: Quadrat name</li> <li>Elevation: Elevation from the UAV DEM</li> <li>Aspect: Aspect from the UAV DEM</li> <li>Ablation: Ablation derived from the UAV products (see the paper for details)</li> <li>Albedo: Measured albedo</li> <li>Rock_stats_image: Statistics on the clasts sampled from the ground truth image of the quadrat</li> <li>Debris_cover: Percentage debris cover derived from both the ground truth image (point) and UAV derived orthophotos</li> <li>Rock_stats_meas: Statistics on the clasts measured in the field</li> </ul> <p>The file Feature_Tracking_Data_Z contains data on clast movement, derived by locating the same clasts in the July and August orthophotos. The August orthophoto location was corrected for ice flow. The main data types are given below. The data sub-types and units are given in the file header.</p> <ul> <li>Coordinates: Locations of the points on the clasts in the July and August orthophotos</li> <li>Elevation: Elevation from the UAV derived DEMs</li> <li>Slope_from_slope_map: Slope from the UAV derived DEMs</li> <li>Surface: Either dirty ice (partial debris cover, or debris (continuous cover)</li> <li>Clast_length: A-axis length of the tracked clast</li> <li>Track_lines_stats: Characteristics of the line joining the clast locations</li> <li>Clast_velocity: Velocity of the tracked clast. Note the velocities derived from the distance measured over the surface were used for the analysis in the paper.</li> </ul> <p>The file Profiles_Data_Z contains the elevation and ablation data extracted following four cross-profiles which crossed the study area. Tables are also included giving the calculated moraine slope gradients and their change over time. The main data types are:</p> <ul> <li>Distance: The distance along the cross-section</li> <li>Elevation: The elevation from either the July or August DEM</li> <li>Ablation: The ablation value from the map of spatially continuous ablation.</li> </ul> <p>The folder GIS_Data_Z contains a range of GIS data associated with the paper. Included in the Zip file is an ArcMap document 'GIS_Data_Dirty_Ice' which includes all the data, along with a Word document ('Metadata for GIS data') which describes each of the files included within the ArcMap document. Briefly, the included data covers ground truth data (georeferenced ground truth photos, sample points and their classification, clast sizes, quadrat georeferencing information), clast track data (over the study area and the small scale clast tracks), stake data (measured and modelled ablation, calculated horizontal velocities and combined slope and emergence values), mapped ablation, percentage debris cover (and the difference in percentage cover), orthophotos, elevation data (and the cross-sections used to extract this) and the boundaries between the dirty and debris-covered ice. All data sets are in the WGS 1984 UTM Zone 32N coordinate system.</p> <p>The folder Percent_Debris_Analysis_H_Z contains the data used to derive Figure 5 (ablation and percentage debris cover data) in Matlab format (.mat file). It also includes the code used to make the figure. More details on file names will not be given here as the code documents the analysis steps.</p> <p>If any further information is required about the data then please get in contact with Catriona Fyffe.</p> <p> </p>
A continuous margin of the Greenland Ice Sheet for the Little Ice Age maximum
<p>The LIA extent was identified and extracted using known techniques of band combinations in remote sensing but applied to look at a terrestrial landscape through a new lens. The marginal zone of the LIA is denoted by little or no vegetation, disturbed sediment, moraines, trimlines, little or no soil development and the exposed rock surfaces are unweathered. Sentinel 2 images were downloaded from USGS Earth Explorer from July- September 2021 to show the peak vegetation season. Scenes with less than 10% cloudiness were chosen. The band combination to identify the LIA extent is B11 (Short Wave Infrared: SWIR), B8 (Near Infrared: NIR), and B2 (Blue). </p><p>The Reclassify Spatial Analyst tool was used to perform an unsupervised classification and geoprocessing to change the value in a raster, from a range to a single value. Image classification is the conversion of a multi-band raster image, such as Sentinel-2, to a single-band raster with defined categories to represent the desired land cover.</p><p>The mask is a visual map of the entire area covered by the GrIS during the LIA maximum. </p><p> The LIA mask was created in projection Stereographic North (ESPG3413) to match the BedMachine product. The data is available for use in 30 x 30m, 150 x 150m and 1 x 1km resolutions in NetCDF Files. It is also available as .tif in 30 x 30m, 150 x 150m and 1 x 1km resolutions and a .shp and .kmz files to be useable in modelling, GIS (Arc & QGIS), and Google Earth. </p>
Supplementary Movies for Pérez-Gussinyé et al. (2023) Synrift and postrift thermal evolution of rifted margins: a re-evaluation of classic models of extension Accepted in GSL Special Publications
<p>Movie S1. Evolution of deformation for the three models shown in Figures 2 and 3. Full extension rate is 10 mm/yr. Sedimentation rate is given in Figures 2 and 3 and Methods (see also Table 1). Red shading shows brittle strain rate, blue shading shows ductile strain rate. The sediments are color coded with age since the start of rifting. The color circles are trackers. The color conventions for the phases are the same as in Figures 1 and 2.</p><p> </p><p>Movie S2. Top: Thermal evolution of the 10 mm/yr full rifting velocity model without sedimentation along with markers (model shown in Figures 2a-e and 3a-c). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. Bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel.</p><p> </p><p>Movie S3. Top: Thermal evolution of the10 mm/yr full rifting velocity model with average sedimentation along with markers (model shown in Figures 2f-j and 3d-g). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. Bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel.</p><p> </p><p>Movie S4. Top: Thermal evolution of the 10 mm/yr full rifting velocity model </p><p>with largest sedimentation along with markers (model shown in Figures 2k-o and 3h-k). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel.</p><p> </p><p> </p><p>Movie S5. Evolution of temperature field for the 10 mm/yr full rifting velocity without sedimentation, (model shown in Figures 2a-e and 3a-c). b) and c) show the geotherms of trackers shown in a) computed from the dynamic model (solid colored lines), and from the ´1-D temperature´ solution (dashed colored lines). The black curve shows the initial geotherm used for the dynamic temperature and for the ´1-D temperature´ calculations. Grey shading indicates strain rate. Note that the horizontal scale is changing to show the rifted margins as the model evolves.</p><p> </p><p>Movie S6. Evolution of temperature field for the 100 mm/yr full rifting model without sedimentation, (model shown in Figures 2a-e and 3a-c). b) and c) show the geotherms of trackers shown in a) computed from the dynamic model (solid colored lines), and from the ´1-D temperature´ solution (dashed colored lines). The black curve shows the initial geotherm used for the dynamic temperature and for the ´1-D temperature´ calculations. Grey shading indicates strain rate. Note that the horizontal scale is changing to show the rifted margins as the model evolves.</p>
Afrotropical, Indo-Malayan, Australasian, Neotropical, and Holarctic (marginally) Regions. in Emballonuridae
Afrotropical, Indo-Malayan, Australasian, Neotropical, and Holarctic (marginally) Regions.
Exploration of transcriptomic and proteomic profiles of the tumor microenvironment of marginal zone lymphoma
GEO Series GSE286927. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
Programmed Death Ligand 1 in Tumor Margins with Implications of Regulation by Interferon Gamma, and a Novel Immuno-Oncologic Transcriptional Signature for Malignant Transformation
GEO Series GSE219053. Homo sapiens. 10 samples. Type: Expression profiling by array.
Exposure to mites sensitizes intestinal stem cell maintenance, splenic marginal zone B cell homeostasis, and heart development to Notch dosage and cooperativity [RNA-seq]
GEO Series GSE149991. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Crosstalk between murine marginal zone B cells, macrophages and CD8alpha+ dendritic cells promotes Listeria monocytogenes infection
GEO Series GSE124771. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Exposure to mites sensitizes intestinal stem cell maintenance, splenic marginal zone B cell homeostasis, and heart development to Notch dosage and cooperativity [ATAC-seq]
GEO Series GSE149990. Mus musculus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Gene expresion profiling of two distnict splenic marginal zone B cell subpopulations, CD80high and CD80low MZ B cells
GEO Series GSE242393. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Validation of a gene signature in histologically normal surgical margins predictive of oral squamous cell carcinoma recurrence
GEO Series GSE108712. Homo sapiens. 575 samples. Type: Expression profiling by array.
Exposure to mites sensitizes intestinal stem cell maintenance, splenic marginal zone B cell homeostasis, and heart development to Notch dosage and cooperativity
GEO Series GSE149992. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Water at the Margins
<p>In many parts of Maputo, accessing water is a challenge. The documentary <em>Water at the margins</em> tells the stories of women and men living <em>at the margins</em> of the water supply network <em>and beyond</em>, where chasing water is the norm. Their stories reveal the many ways in which water deeply marks people’s life and their ability to live and settle in a place, take care of their family or develop a business. As these stories unfold, the question of why so many in Maputo are unserved or undeserved becomes more urgent. Water utility staff are faced with this question: is it possible to provide water more equitably? <em>Water at the margins</em> is produced by Whales that Fly and directed by Maria Rusca. It has received funding from EU Horizon 2020 under the Marie Curie project INHAbIT cities (No. 656738) by IHE Delft-DUPC.</p>
Dataset related to article "The Role of Pathological Method and Clearance Definition for the Evaluation of Margin Status after Pancreatoduodenectomy for Periampullary Cancer. Results of a Multicenter Prospective Randomized Trial."
<p>This record contains data related to article "The Role of Pathological Method and Clearance Definition for the Evaluation of Margin Status after Pancreatoduodenectomy for Periampullary Cancer. Results of a Multicenter Prospective Randomized Trial."</p> <p>Background: There is extreme heterogeneity in the available literature on the determination of R1 resection rate after pancreatoduodenectomy (PD); consequently, its prognostic role is still debated. The aims of this multicenter randomized study were: to evaluate the effect of sampling and clearance definition in determining R1 rate after PD for periampullary cancer; to assess the prognostic role of R1 resection.</p> <p>Methods: PD specimens were randomized to Leeds Pathology Protocol (LEEPP) (group A) or conventional method adopted before the study (group B). R1 rate was determined adopting 0 and 1 mm clearance; the association between R1, local recurrence (LR) and overall survival (OS) was also evaluated.</p> <p>Results. One-hundred-sixty-eight PD specimens were included. With 0 mm clearance, R1 rate was 26.2% and 20.2% for groups A and B, respectively; with 1 mm, R1 rate was 60.7% and 57.1%, respectively (p > 0.05). Only in group A, R1 was found to be a significant prognostic factor: at 0 mm, median OS was 36 and 20 months for R0 and R1, respectively, while at 1 mm. median OS was not reached and 30 months. At multivariate analysis, R1 resection was found as a significant prognostic factor independent of clearance definition only in case of adoption of LEEPP.</p> <p>Conclusions. The 1 mm clearance is the most effective factor in determining R1 rate after PD. However, the pathological method is crucial to accurately evaluate its prognostic role: only R1 resections obtained with the adoption of LEEPP seem to significantly affect prognosis.</p>
ScienceDex guides
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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.