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30,948 results for “Profile”
CAM Global RCE simulations TC track, radial profiles, and filtered precipitation files
<p>This dataset includes processed output from the Community Atmosphere Model (CAM), version 5, the atmospheric component of the Community Earth System Model (CESM2). CAM was run in a global rotating radiative convective equilibrium (RCE) aquaplanet configuration for 2 years, with the first 2 months discarded to allow for spin-up. The globally-uniform sea surface temperature (SST) was varied from 295 to 305 K in 1 K increments, producing a total of 11 model simulations. More details about CAM and the RCE configuration can be found in the associated manuscript in <em>JGR: Atmospheres.</em> The TempestExtremes software package (https://github.com/ClimateGlobalChange/tempestextremes) was used to track tropical cyclones (TCs) in the raw model output. Specifically DetectNodes and StitchNodes were used to locate potential TCs based on sea level pressure minima and then stitch these TC candidates into tracks based on spatial proximity. NodeFileEditor was used to calculate a radial wind profile at each timestep in each TCs' lifetime, and from these radial profiles, the TCs' outer sizes were estimated based on the radii of 8 m/s winds, outside the radii of maximum winds. Lastly NodeFileFilter was used to extract all precipitation within these calculated outer sizes at each timestep in each TC's lifetime. This dataset contains the TC track files, the TC radial wind profile and outer size files, and the filtered TC precipitation files. Note that while the track and radial profile files contain data from TCs all over the global domain, the filtered TC precipitation files only contain precipitation froms TCs between 40°S and 40°N because that's the domain we used for the TC precipitation analysis in the manuscript. </p>
Data from: Combined analysis of micro RNA and proteomic profiles and interactions in patients with primary lung adenocarcinoma and lung adenocarcinoma brain metastases
<p>We carried out an analysis of miRNAs expression profiles and protein spectrums of non-metastatic primary lung adenocarcinoma (LP) and patients with brain metastases (BM) to better explore the molecular basis of BM. Files containing raw data of miRNA expression and proteomic profiles in the manuscript "Combined analysis of micro RNA and proteomic profiles and interactions in patients with primary lung adenocarcinoma and lung adenocarcinoma brain metastases".</p>
Early Detection Of Cardiovascular Risk Factors And Definition Of Psychosocial Profile In Women Through A Systematic Approach: The Monzino Women Heart Center's Experience
<p>The raw dataset is related to the analysis of CV risk factors and psychosocial profile in women enrolled in a CV prevention program </p>
Gene expression profiles compared in environmental and malnutrition enteropathy in Zambian children and adults
<p class="CxSpFirst"><span><span><span><span><span><span><span><span><span><span><span><strong>Background:</strong> Environmental enteropathy (EE) contributes to growth failure in millions of children worldwide, but its relationship to clinical malnutrition has not been elucidated. We used RNA sequencing to compare duodenal biopsies from adults and children with EE, and from children with severe acute malnutrition (SAM), to define key features of these malnutrition-related enteropathies.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><strong>Methods:</strong> RNA was extracted and sequenced from biopsies of children with SAM in hospital (n=27), children with non-responsive stunting in the community (n=30), and adults living in the same community (n=37) using an identical sequencing and analysis pipeline. Two biopsies each were profiled and differentially expressed genes (DEGs) were computed from the comparisons of the three groups. DEG lists from these comparisons were then subjected to analysis with CompBio software to assemble a holistic view of the biological landscape and IPA software to interrogate canonical pathways.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><strong>Findings:</strong><i> </i>Dysregulation was identified in goblet cell/mucin production and xenobiotic metabolism/detoxification for both cohorts of children, versus adults. Within the SAM cohort, substantially greater induction of immune response and barrier function, including NADPH oxidases was noted, concordant with broadly reduced expression of genes associated with the brush border and intestinal structure/transport/absorption. Interestingly, down regulation of genes associated with the hypothalamic-pituitary-adrenal axis was selectively observed within the cohort of children with stunting.</span></span></span></span></span></span></span></span></span></span></span></p> <p><strong>Interpretation:</strong><span><span><span><span><span><span><span><span><span><span><span> Gene expression profiles in environmental enteropathy and severe acute malnutrition have similarities, but SAM has several distinct transcriptional features. The intestinal capacity to metabolise drugs and toxins in malnourished children requires further study.</span></span></span></span></span></span></span></span></span></span></span></p>
A photo of the typical view of organic, permaculture and conventional horticultural farms and a photo of a typical soil profile in a core sampler for each
<p>The pdf file contains an introduction slide (Slide 1) with the list of the introduced farms.</p> <p>There are 15 slides following the introductory file.</p> <p>Each slide has a photo of a horticultural farm, its code and a photo of one of its typical soil profiles.</p> <p>The photo of the profile was made of a soil core sampler that is 100 cm long.</p> <p>In some of the farms, there were multiple profiles revealed and photos were made, here we just show one of these.</p> <p>The majority of the soils are Luvisols but we have some 2 Fluvisols, 2 Chernozems and 2 Fluvisols.</p> <p>More information can be found in a published article: Szilágyi, A.; Plachi, E.; Nagy, P.; Simon, B.; Centeri, C. Assessing Earthworm Populations in Some Hungarian Horticultural Farms: Comparison of Conventional, Organic and Permaculture Farming. <em>Biol. Life Sci. Forum</em> <strong>2021</strong>, <em>2</em>, 11. https://doi.org/10.3390/BDEE2021-09416</p> <p>The purpose of the recent pdf is to provide information for an upcoming article in the journal of Diversity.</p> <p>All soil laboratory analyses have already been published for this purpose:</p> <p>https://zenodo.org/record/5717449#.YeoUYv7MJPY</p>
Genome-wide DNA methylation profiling identifies epigenetic signatures of β-lactams induced fatal anaphylactic shock
<p>Drug hypersensitivity is one of the most frequent causes of anaphylaxis in adults, of which antibiotics are the most common culprits, particularly β-lactams induced anaphylactic shock deaths. We provided the genome wide DNA methylation profiling study of PBMC from 14 individuals peripheral venous blood samples. Illumina Infinium Human Methylation EPIC BeadChip was used. Among the 14 individuals, 8 patients were died from β-lactams induced anaphylactic shock, 6 healthy individuals were controls. Inclusion criteria for the β-lactams induced anaphylactic shock as follows: ①A clear history of β-lactams transfusion or components of β-lactams detected from blood (or skin of suspicious infusion site); ②Shock symptoms occurred after infusion of β-lactams and died within a short period of time; ③The immunohistochemical results of throat, lung and gastrointestinal tissues showed that tryptase or chymase were mostly positive expression; ④ Excluding other causes (disease, poisoning) of death; ⑤ No decay occurred in the corpse.Inclusion criteria for controls as follows: ① Healthy, no previous common underlying diseases and no history of genetic disease; ② No history of allergies to drugs, food, pollen and so on; ③Skin prick test (SPT) is negative, allergen-specific IgE (sIgE)< 100IU/mL; the skin test results of β-lactam drugs were negative.</p>
Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study
<p>Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones.</p> <p>Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)</p> <p> </p>
Coded data and R scripts for the article-Toward a dynamic behavioral profile of the Mandarin Chinese temperature term re
<p>These are the coded dataset and R scripts for the article "Towards a dynamic behavioral profile of Mandarin Chinese temperature term re: A diachronic semasiological approach".</p>
Quartz solubility profiles
<p>Table S1 describes the solubility of quartz in pure H<sub>2</sub>O (salinity = 0 wt%) under hydrostatic conditions in the Yuzawa geothermal field.</p> <p>Table S2 describes the solubility of quartz in saline fluid (5 wt% NaCl equivalent) under lithostatic conditions in the Yuzawa geothermal field.</p> <p> </p>
Pultruded carbon fiber profiles - 3D x-ray tomography data-sets for two different pultruded profiles
<p>3D X-ray scan on Zeiss Xradia 520</p> <table> <tbody> <tr> <td> <table> <tbody> <tr> <td> </td> <td>A1</td> <td>A2</td> <td>A2S</td> <td>B1</td> <td>B1S</td> <td>B2</td> <td>B3</td> </tr> <tr> <td>Scanning Voxel size [μm]</td> <td>1.9767</td> <td>1.97</td> <td>1.9731</td> <td>1.9751</td> <td>1.9753</td> <td>1.9771</td> <td>1.9752</td> </tr> <tr> <td>FoV: (x; y; z) [mm]</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> </tr> <tr> <td>Accelerating Voltage [kV]</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> </tr> <tr> <td>Power [W]</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> <tr> <td>Filter</td> <td>Air</td> <td>LE1</td> <td>LE1</td> <td>LE1</td> <td>LE1</td> <td>Air</td> <td>LE1</td> </tr> <tr> <td>Optical magnification</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> </tr> <tr> <td>Detector to sample distance [mm]</td> <td>25.5</td> <td>25.52</td> <td>26.82</td> <td>25.4</td> <td>26.75</td> <td>25.5</td> <td>25.8</td> </tr> <tr> <td>Source to sample distance [mm] </td> <td>10.6</td> <td>10.61</td> <td>11.11</td> <td>10.5</td> <td>11.1</td> <td>10.6</td> <td>10.7</td> </tr> <tr> <td>Exposure time [s]</td> <td>15</td> <td>18</td> <td>20</td> <td>18</td> <td>18</td> <td>15</td> <td>18</td> </tr> <tr> <td>No. of projections</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> </tr> <tr> <td>Rotation</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> </tr> <tr> <td>Binning</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> <tr> <td>Total scanning time [h]</td> <td>26</td> <td>33</td> <td>33</td> <td>32</td> <td>32</td> <td>26</td> <td>29</td> </tr> <tr> <td>File-size [GB]</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> </tbody> </table> </td> </tr> </tbody> </table>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
Aircraft profiles of stable isotope ratios in atmospheric total and condensed water from the NASA ORACLES mission.
<p>Aircraft in-situ measurements of water concentration and heavy water isotope ratios D/H and 18O/16O of cloud water and total water (water vapor plus condensed water) were collected during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project. Aircraft sampling took place in the southeast Atlantic marine boundary layer and lower troposphere (equator to 22 degrees south) over the months of Sept. 2016, Aug. 2017, and Oct. 2018. Isotope measurements were made using cavity ring-down spectroscopic analyzers integrated into the Water Isotope System for Precipitation and Entrainment Research (WISPER). The WISPER data are processed into mean latitude-altitude curtains and individual vertical profiles for each sampling period.</p> <p> </p> <p>The WISPER data accompanied a suite of other variables including standard meteorological quantities (wind, temperature, moisture), trace gas and aerosol concentrations, radar, and lidar remote sensing, which can be accessed through the DOIs listed further down. The ORACLES campaigns are described by Redemann et al., (2021). The water isotope measurements are further described in Henze et al., (2021). The absolute error with respect to the SMOW-SLAP scale is explained in detail by Henze et al., (2021).</p> <p> </p> <p>Total water concentration and isotope ratios were binned and averaged onto latitude-altitude grids using a kernel estimation approach, with weighting designed to estimate the mean during the approximate month-long duration of each sampling period. Standard deviations for each bin are also computed using kernel density estimation.</p> <p> </p> <p>Time intervals during aircraft vertical profiling are isolated and averaged onto 50-meter vertical levels. The files include water concentration and isotope ratios for both total water and cloud water in addition to temperature, pressure, latitude, and longitude.</p> <p> </p> <p>See included file README.txt for additional details.</p> <p> </p> <p>References</p> <p>---------------</p> <p>Henze, D., Noone, D., and Toohey, D.: Aircraft measurements of water vapor heavy isotope ratios in the marine boundary layer and lower troposphere during ORACLES, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-238, in review, 2021.</p> <p> </p> <p>Redemann, J., Wood, R., Zuidema, P., Doherty, S. J., Luna, B., LeBlanc, S. E., Diamond, M. S., Shinozuka, Y., Chang, I. Y., Ueyama, R., Pfister, L., Ryoo, J.-M., Dobracki, A. N., da Silva, A. M., Longo, K. M., Kacenelenbogen, M. S., Flynn, C. J., Pistone, K., Knox, N. M., Piketh, S. J., Haywood, J. M., Formenti, P., Mallet, M., Stier, P., Ackerman, A. S., Bauer, S. E., Fridlind, A. M., Carmichael, G. R., Saide, P. E., Ferrada, G. A., Howell, S. G., Freitag, S., Cairns, B., Holben, B. N., Knobelspiesse, K. D., Tanelli, S., L'Ecuyer, T. S., Dzambo, A. M., Sy, O. O., McFarquhar, G. M., Poellot, M. R., Gupta, S., O'Brien, J. R., Nenes, A., Kacarab, M., Wong, J. P. S., Small-Griswold, J. D., Thornhill, K. L., Noone, D., Podolske, J. R., Schmidt, K. S., Pilewskie, P., Chen, H., Cochrane, S. P., Sedlacek, A. J., Lang, T. J., Stith, E., Segal-Rozenhaimer, M., Ferrare, R. A., Burton, S. P., Hostetler, C. A., Diner, D. J., Seidel, F. C., Platnick, S. E., Myers, J. S., Meyer, K. G., Spangenberg, D. A., Maring, H., and Gao, L.: An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin, Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, 2021.</p> <p> </p> <p>The complete archive of ORACLES data are accessible via the digital object identifiers (DOIs) provided under ORACLES Science Team references as follows:</p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2018, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2018_V3, 2020a. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2017, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2017_V3, 2020b. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2016_V3, 2020c. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard ER2 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/ER2/2016_V3, 2020d.</p>
miRNA profiling of human nasopharyngeal carcinoma cell lines HONE1 and CNE2 after X-ray therapy
<p>The package contains two file:</p> <p>Supplementary Table 1. Kruskal Wallis test for cell viability rate</p> <p>Supplementary Table 2. Kruskal Wallis test for apoptosis rate</p> <p>The HONE1 and CNE2 cells were irradiated with X-rays at doses of 4 Gy, 8 Gy, 16 Gy and 20 Gy for 24 h. The cell viability rate and apoptosis rate were compared.</p>
Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles
<p>consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets</p>
Modeling and validating a SuperDARN radar's Poynting flux profile
<p>Numerical ray trace modelingl output corresponding to Radio Science manuscript "Modeling and validating a SuperDARN radar's Poynting flux profile" by G. W. Perry.</p>
Measurements of water levels and water temperature profiles in Lake Kinneret, in summer, during the period (2000 – 2020)
<p><em>The datasets include measurements of water levels and water temperature profiles in Lake Kinneret, in summer, during the period (2000 – 2020). </em><em>Lake Kinneret is located in Israel. </em>Shipboard measurements of water temperature profiles were taken at the deepest point of ~40 m near the lake center (32.82 N; 35.60 E). The <em>AML MINOS X probe was used (specifications are available online at <a href="https://geo-matching.com/ctd-systems/minos-x-ctd/svp">https://geo-matching.com/ctd-systems/minos-x-ctd/svp</a>). </em><em>Data of water temperature profiles are associated with the Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research (</em><a href="http://kinneret.ocean.org.il/ar_3d_vb.aspx"><em>http://kinneret.ocean.org.il/ar_3d_vb.aspx</em></a><em> ). Data of Lake Kinneret water levels are associated with the Israel Water and Sewage Authority (</em><a href="https://www.gov.il/en/departments/water_authority/govil-landing-page">https://www.gov.il/en/departments/water_authority/govil-landing-page</a><em> ). Data format: xlsx file. </em>There are two sheets in the Excel file: the first sheet includes water temperature profiles, while the second sheet includes lake water levels, in summer, during the period (2000 – 2020). </p>
Effect of heterogeneous substrate adhesivity of follower cells on speed and tension profile of leader cells in primary keratocyte collective cell migration
<p><span>In single keratocyte motility, membrane tension is reported to be high at cell-fronts and believed to establish front coherence. To understand role of membrane mechanics in collective cell migration, we study membrane height fluctuations in cell sheets from fish scales using interference reflection microscopy (IRM). We report the monolayer to have cells lacking substrate adhesion and show that such "non-sticky" cells can form bridges between leader cells and far-away follower cells. Do such interactions alter motility and membrane mechanics in such leaders? We find non-significant, but reduced speed for leaders with "non-sticky" followers in comparison to other leaders. Cells show high phenotypic variability in their membrane fluctuation tension profiles. On average, this tension is found to be lower at cell fronts than the mid-section. However, leaders with non-sticky followers are more prone to display higher tension at their front and have a negative correlation between cell speed and front-mid tension difference. We, thus, conclude that intracellular tension gradients are heterogeneous in cell sheets and substrate adhesivity of followers can control the coupling of the gradient to cell speed.</span></p>
Raw Data for the article: Liver Transplantation for Unresectable Intrahepatic Cholangiocarcinoma: The Role of Sequencing Genetic Profiling
<p>Intrahepatic cholangiocarcinoma (iCCA) is a rare and aggressive primary liver tumor, characterized by a range of different clinical manifestations and by increasing incidence and mortality rates even after curative treatment with radical resection. In recent years, growing attention has been devoted to this disease and some evidence supports liver transplantation (LT) as an appropriate treatment for intrahepatic cholangiocarcinoma; evolving work has also provided a framework for better understanding the genetic basis of this cancer. The aim of this study was to provide a clinical description of our series of patients complemented with Next-Generation Sequencing genomic profiling. From 1999 to 2021, 12 patients who underwent LT with either iCCA or a combined hepatocellular and cholangiocellular carcinoma (HCC-iCCA) were included in this study. Mutations were observed in gene activating signaling pathways known to be involved with iCCA tumorigenesis (KRAS/MAPK, P53, PI3K-Akt/mTOR, cAMP, WNT, epigenetic regulation and chromatin remodeling). Among several others, a strong association was observed between the Notch pathway and tumor size (point-biserial <em>rho<sub>pb</sub></em> = 0.93). Our results are suggestive of the benefit potentially derived from molecular analysis to improve our diagnostic capabilities and to devise new treatment protocols, and eventually ameliorate long-term survival of patients affected by iCCA or HCC-iCCA.</p>
Shapefiles for Glen Torridon Bedrock Ridges, Ripples, Transverse Aeolian Ridges, Wavelength, and Topographic Profiles
<p>Zipfiles containing polyline shapefiles produced with ESRI's ArcMap 10.6 tracing the location and extent of ridges and transverse aeolian ridges (TARs) in the Glen Torridon region of Aeolis Mons (informally Mount Sharp), Gale crater, Mars. </p>
Global 1° × 1° map of vertical POC flux profiles simulated by the MSPACMAM
<p>Model output data generated by two different configurations of the MSPACMAM: (1) the standard model configuration (so-called <em>std</em> configuration) and (2) the model configuration including large particle fragmentation (so-called <em>frag</em> configuration). The MSPACMAM simulates the climatological annual mean state of sinking particulate organic carbon (POC) fluxes in the contemporary global ocean. Model variables include small particle POC concentration, large particle POC concentration, small particle sinking speed, large particle sinking speed, sinking POC flux, and POC transfer efficiency at 1000 m (so-called <span class="math-tex">\(T_{eff}\)</span>).</p>
ScienceDex guides
Understand access before you commit
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.