Find research datasets worth reusing

Lipidomics of exosomes derived from granulosa cells

The mapped data files generated from the analysis of the pathway in MetExploreViz. Lipid content categorized by saturation level.

openmit-licenseJun 2024View details →

Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model

data.tar.gz contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:   ./marvel: - This directory contains rds and Rdata objects that were created using the MARVEL R package cell_type_goresults.rds - This is the go results split by cell type marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting normalized_sj_expression.Rds - This object is the normalized splice junction expression Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition   ./seurat: - This directory contains all intermediate and final Seurat single-cell gene expression objects annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses. clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers. filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA. filtered_brain_samples.rds - This is the filtered Seurat object, before PCA. integrated_brain_samples.rds - This the integrated Seurat object, before other steps.   ./star: - All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space. J1/ - This directory contains outputs for brain sample J1 J13/ - This directory contains outputs for brain sample J13 J15/ - This directory contains outputs for brain sample J15 J2/ - This directory contains outputs for brain sample J2 J3/ - This directory contains outputs for brain sample J3 J4/ - This directory contains outputs for brain sample J4 K1/ - This directory contains outputs for kidney sample K1 K2/ - This directory contains outputs for kidney sample K2 K3/ - This directory contains outputs for kidney sample K3 K4/ - This directory contains outputs for kidney sample K4 K5/ - This directory contains outputs for kidney sample K5 K6/ - This directory contains outputs for kidney sample K6   ./star/genome: - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a> chrLength.txt chrNameLength.txt chrName.txt chrStart.txt exonGeTrInfo.tab exonInfo.tab geneInfo.tab Genome genomeParameters.txt Log.out SA SAindex sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab   ./star/J1: - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a>  Log.final.out Log.out Log.progress.out SJ.out.tab Solo.out/ STARgenome/   ./star/J1/Solo.out:- This directory contains the outputs used for downstream analysis Barcodes.stats GeneFull_Ex50pAS/ SJ/   ./star/J1/Solo.out/GeneFull_Ex50pAS: - This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns) Features.stats filtered/ raw/ Summary.csv UMIperCellSorted.txt   ./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: - This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages) barcodes.tsv.gz - This file contains filtered cell barcodes features.tsv.gz - This file contains filtered features (genes) matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix   ./star/J1/Solo.out/GeneFull_Ex50pAS/raw: - This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered. barcodes.tsv features.tsv matrix.mtx   ./star/J1/Solo.out/SJ: - This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression Features.stats raw/ Summary.csv   ./star/J1/Solo.out/SJ/raw: - This directory contains the raw barcodes, features, and matrix files for splice junction expression barcodes.tsv - This file contains filtered cell barcodes features.tsv - This file contains filtered features (splice junctions) matrix.mtx - This file contains the filtered cell by gene expression count matrix   ./star/J1/_STARgenome: - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from<a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"> https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a>  exonGeTrInfo.tab exonInfo.tab geneInfo.tab sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab

A quantitative approach to easily characterize and predict cell death and escape to β-lactam treatments

This repository contains the raw experimental data corresponding to the paper entitled "A quantitative approach to easily characterize and predict cell death and escape to β-lactam treatments" and authored by Andreani, Gross and colleagues.   The analysis of the data and the creation of the figures of the paper can be done using the code available at https://gitlab.inria.fr/InBio/Public/esbl-escape. A README file, briefly describing data organization, is provided.

TGF-β neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice

Deep TCR sequencing was performed using the TCR Profiling Kit from MiLaboratories (Mouse α/β TCR RNA; Kit MiLaboratories; TMMR-001). Deep TCR sequencing was analyzed using the MiXCR software from MiLaboratories per manufacturer's recommendations. The files correspond to the TCR-beta sequences. The files are named as follows:   <table> <tbody> <tr> <td>file_name</td> <td>cell type sequenced</td> <td>tissue of origin</td> <td>treatment</td> <td>mouse_id</td> </tr> <tr> <td>21BA1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>23BA2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>25BA3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>27BA4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>29BA5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>22BA1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>24BA2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>26BA3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>28BA4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>30BA5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>31CON1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>6</td> </tr> <tr> <td>33CON2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>7</td> </tr> <tr> <td>35CON3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>8</td> </tr> <tr> <td>37CON4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>9</td> </tr> <tr> <td>39CON5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>10</td> </tr> <tr> <td>32CON1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>6</td> </tr> <tr> <td>34CON2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>7</td> </tr> <tr> <td>36CON3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>8</td> </tr> <tr> <td>38CON4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>9</td> </tr> <tr> <td>40CON5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>10</td> </tr> <tr> <td>1aPDL11dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>3aPDL12dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>5aPDL13dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>7aPDL14dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>9aPDL15dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>2aPDL11SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>4aPDL12SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>6aPDL13SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>8aPDL14SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>10aPDL15SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>11aTGFB1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>13aTGFB2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>15aTGFB3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>17aTGFB4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>19aTGFB5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> <tr> <td>12aTGFB1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>14aTGFB2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>16aTGFB3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>18aTGFB4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>20aTGFB5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> </tbody> </table>

Analysis of public single-cell sequencing database of COVID lung samples

Lung endothelial cells from three published scRNA-seq datasets (GSE122960, GSE149878, GSE171668) of healthy subjects and COVID-19 patients were collected for further integrative analyses. The endothelial cells were classified into three sub-groups according to their distinguished expression of IL7R, DKK2, and EDNRB. For differential analysis of gene expression, counts per million of aggregated UMIs in each group were adopted in Wilcoxon rank-sum test.

opencc-by-4.0May 2023View details →

Novel stereological method for estimation of cell counts in 3D collagen scaffolds

Dataset provides all images used for cell number evaluation. The used macros are the part of Supplementary of the article.

One-cell-stage to 21-somite-stage development of a zebrafish embryo

These videos show the development of a zebrafish embryo from one-cell-stage to 21-somite-stage. Unedited videos, as well as a sped-up version are available. The sped-up version includes text indicating the developmental stages, as well as how many hours post fertilization each stage occurs at a temperature of 28.5°C. Videos were recorded and edited by Mona Wellhäusser and Timo Schreiber in the research group headed by Lennart Hilbert at the Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology. Videos were acquired using an Olympus SZX7 zoom stereo transmitted light microscope, equipped with an Olympus SZX2-ILLTQ base, an Olympus DF Plapo 1X-4 stereo objective, as well as an Olympus EP50 camera. The imaging dish was constructed from a 35 mm glass bottom dish glued into the middle of a 90 mm Petri dish using clear nail polish. Fertilized zebrafish eggs were placed into the glass bottom dish, the imaging dish was filled to the brim with E3 medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, 0.33 mM MgSO4), then sealed with an upside-down Petri dish lid for recording. 'Microscopy_Setup.png' illustrates the microscopy dish construction (illustration created in biorender.com).

STING OPS: BJ1 Secondary Screen Single-Cell Features

Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for BJ1 fibroblast secondary screen. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_BJ1. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.

opencc-zeroJul 2024View details →

dryad36/100

Data from: Drosophila medulla neuroblast termination via apoptosis, differentiation and gliogenic switch is scheduled by the depletion of the neuroepithelial stem cell pool

The brain is consisted of diverse neurons arising from a limited number of neural stem cells. Drosophila neural stem cells called neuroblasts (NBs) produces specific neural lineages of various lineage sizes depending on their location in the brain. In the Drosophila visual processing centre - the optic lobes (OLs), medulla NBs derived from the neuroepithelium (NE) give rise to neurons and glia cells of the medulla cortex. The timing and the mechanisms responsible for the cessation of medulla NBs are so far not known. In this study, we show that the termination of medulla NBs during early pupal development is determined by the exhaustion of the NE stem cell pool. Hence, altering NE-NB transition during larval neurogenesis disrupts the timely termination of medulla NBs. Medulla NBs terminate neurogenesis via a combination of apoptosis, terminal symmetric division via Prospero, and a switch to gliogenesis via Glial Cell Missing (Gcm), however, these processes occur independently of each other. We also show that temporal progression of the medulla NBs is mostly not required for their termination. As the Drosophila OL shares a similar mode of division with mammalian neurogenesis, understanding when and how these progenitors cease proliferation during development can have important implications for mammalian brain size determination and regulation of its overall function.

Resolving single-cell expression profiles by pseudo-temporal integration of transcriptomic and proteomic datasets.

Raw and processed single cell proteomics (scp-MS) and scRNA-Seq data of HEK293-PIP-FUCCI cells which were challanged with hypoxia. The repository contains data for recreating the pseudo-temporal alignment analysis of transcription-translation profiles.

STING OPS: HeLa cGAMP (6 hours) Secondary Screen Single-Cell Features

<div> Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for HeLa secondary screen, 6 hours post-cGAMP, with or without rolling ball background subtraction. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.   </div>

opencc-by-4.0May 2024View details →

Figure 1 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry

Figure 1. Proportion of serine, threonine, and tyrosine in phosphorylation sites.

dryad36/100

Single cell multiomic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.

opencc-zeroJul 2024View details →

Single-cell transcriptomic analysis of B cells reveals new insights into atypical memory B cells in COVID-19

Here, we performed single-cell RNA sequencing of S1 and RBD protein-specific B cells from convalescent COVID-19 patients with different clinical manifestations. This study aimed to evaluate the role and developmental pathway of atypical memory B cells in response to SARS-CoV-2 infection. The results revealed a proinflammatory signature across B cell subsets associated with disease severity, as evidenced by the upregulation of genes such as GADD45B, MAP3K8, and NFKBIA in critical and severe individuals. Furthermore, the analysis of atypical memory B cells suggested a developmental pathway similar to that of conventional memory B cells through germinal centers, as indicated by the expression of several genes involved in germinal center processes, including CXCR4, CXCR5, BCL2, and MYC. Additionally, the upregulation of genes characteristic of the immune response in COVID-19, such as ZFP36 and DUSP1, suggested that the differentiation and activation of atypical memory B cells may be influenced by exposure to SARS-CoV-2 and that these genes may contribute to the immune response for COVID-19 recovery. Our study contributes to a better understanding of atypical memory B cells in COVID-19 and the role of other B cell subsets across different clinical manifestations.

STING OPS: HeLa Unstimulated Secondary Screen Single-Cell Features

<div> <div> Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for HeLa secondary screen, unstimulated, without rolling ball background subtraction. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.   </div>   </div>

3D micro/nano-CT datasets of sandstone rock, lithium-ion battery, and fuel cell used for testing P3T-Net

These are the datasets used for training and testing P3T-Net for 3D unpaired domain transfer in .tif format. Images can be directly opened with ImageJ, Avizo, Python, Matlab, etc. Dataset contains overall 8 3D images of 4 cases. Each case contains images from the target domain and source domain.  Case 1. Target domain: micro-CT image of a 9-hour long scan of a sandstone (voxel size: 2.15um); Source domain: micro-CT image of a 7-minute fast scan of a sandstone (voxel size: 2.15um). Case 2. Target domain: micro-CT image of a 7-hour long scan of a sandstone (voxel size: 3.28um); Source domain: synchrotron micro-CT image of a 24-second fast scan of a sandstone (voxel size: 6.75um). Case 3. Target domain: nano-CT image of a dual-mode scan of Lithium-ion battery cathode (voxel size: 128nm); Source domain: three nano-CT images of a single-mode scan of lithium-ion battery cathode (two absorption and one phase mode) (voxel size: 128nm). Case 4. Target domain: micro-CT image of a 3-hour long scan of a fuel cell (voxel size: 2.25um); Source domain: micro-CT image of a 10-minute fast scan of a fuel cell (voxel size: 2.25um). For any further questions or requirements, please contact kunning.tang@unsw.edu.au.

STING OPS: HeLa Unstimulated Secondary Screen Single-Cell Features (Background-Subtracted)

<div> <div> Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for HeLa secondary screen, unstimulated, with rolling ball background subtraction. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.   </div>   </div>

STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 2/5)

<div> <div> Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 2/5. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README. </div> </div>

STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 5/5)

<div> <div> Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 5/5. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README. </div> </div>

STING OPS: HeLa cGAMP (4 hours) Secondary Screen Single-Cell Features

<div> <div> Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening Single-cell features and coordinates for HeLa secondary screen, 4 hours post-cGAMP, with or without rolling ball background subtraction. Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa. README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README. </div> </div>