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Experimental results and the user questionnaires for KG profiling tool (ABSTAT vs Protégé) evaluation
<p>Experimental results and the user questionnaires for KG profiling tool (ABSTAT vs Protégé) evaluation</p>
[DATA] Amplicon-based gene expression profiling of blood from mice stimulated with R848 and treated with TLR7/8 inhibitor MHV370
<p>129/Sv mice (5 per group) were treated with daily R848 i.p. (2.5 mg/kg) for 14 days. From day 7 onward, one group received oral doses of 15 mg/kg MHV370 b.i.d., one group vehicle. At day 14, both groups were compared to naive 129/Sv mice. Gene expression profiling of blood was performed via amplicon sequencing (Ion AmpliSeq Transcriptome Mouse Gene Expression Kit, Thermo Fisher Scientific).</p> <p>Raw amplicon counts, sample metadata and gene information are provided in tab-separated value (TSV) files counts.tsv, samples.tsv and genes.tsv, respectively.</p> <p>The entire dataset is also provided as a DGEList object saved as an RDS file (dge_unfiltered.rds) that can be explored using R (package edgeR).</p> <p>The respective code for analysis can be found under DOI: 10.5281/zenodo.7575672</p>
Sargassum sp extract improve Hematological profile of Tilapia fish (Oreochromis niloticus)
<p>Strategies to increase body resistance and prevent disease in cultivation include using vaccines, antibiotics, and probiotics. Today, the use of antibiotics with natural ingredients is becoming a trend. One of the natural ingredients that contain high antioxidants and antibiotics is <em>Sargassum</em> sp. This research was conducted from March to May 2022 at the Biotechnology Laboratory, Faculty of Fisheries and Marine, Universitas Riau. This research was conducted in two stages, namely 1) the sensitivity of extracts of <em>Sargassum</em> sp. and 2) the application of <em>Sargassum</em> sp. extract orally in tilapia (<em>O. niloticus</em>). The results showed that the extract of <em>Sargassum</em> sp. was able to inhibit the growth o<em>f Aeromonas hydrophila </em>bacteria with a clear zone of 6.5-15.0 mm, which is classified as resistant. At doses of 2000, 2500, and 3000 ppm, it did not cause death in fish for 96 hours (LD<sub>50</sub>). Hematological parameters can be a sign of the health status of fish. Tilapia given to the addition of <em>Sargassum</em> sp. with different doses gave an effect between treatments, both after 30 days of rearing and post-test against <em>A.hydrophila</em> bacteria (p<0.05). The results showed that the hematology of fish fed with <em>Sargassum</em> sp. extract was in the normal or healthy range. Healthy tilapia had erythrocyte counts ranging from 1.34-2.11 x10<sup>6</sup> cells/mm<sup>3</sup>, hematocrit 26.17-33.19%, hemoglobin 6.26-11.2 g/dL and total leukocytes 1.01-1.50 x10<sup>4</sup> cells/mm<sup>3</sup> and total erythrocytes 5.88-9.13x10<sup>4</sup> cells/ mm<sup>3</sup>. A dose of 3000 ppm provided the highest health improvement against <em>A.hydrophila</em> bacterial infection</p>
Non-targeted metabolome profiling in splenic monocyte-derived dendritic cells from Plasmodium chabaudi-infecetd mice
<p>Spleens from mice infected with Plasmodium chabaudi were processed and stained for localization of monocyte-derived dendritic cells (MODCs) by flow cytometry. The markers utilized were: Live/Dead, F4/80, CD11b, DCSign, MHCII, CD11c and CD3. After sorted out, MODCs were frozen in liquid nitrogen, followed by metabolite extraction, as recommended by The Metabolomics Facility at MD Anderson. Metabolites were extracted using ice-cold 0.1% Ammonium hydroxide in 80/20 (v/v) methanol/water. Extracts were centrifuged at 17,000 g for 5 min at 4°C, and supernatants were transferred to clean tubes, followed by evaporation to dryness under nitrogen. Dried extracts were reconstituted in deionized water, and 5 μL was injected for analysis by ion chromatography (IC)-MS. IC mobile phase A (MPA; weak) was water, and mobile phase B (MPB; strong) was water containing 100 mM KOH. A Thermo Scientific Dionex ICS-5000+ system included a Thermo IonPac AS11 column (4 µm particle size, 250 x 2 mm) with column compartment kept at 30°C. The autosampler tray was chilled to 4°C. The mobile phase flow rate was 350 µL/min and gradient from 1mM to 100mM KOH was used. The total run time was 60 min. To assist the desolvation for better sensitivity, methanol was delivered by an external pump and combined with the eluent via a low dead volume mixing tee. Data were acquired using a Thermo Orbitrap Fusion Tribrid Mass Spectrometer under ESI negative ionization mode at a resolution of 240,000.</p>
Fig. 1. A Eudrilus eugeniae specimen from a in Eco-taxonomic profile of an iconic vermicomposter - the 'African Nightcrawler' earthworm, Eudrilus eugeniae (Kinberg, 1867)
Fig. 1. A Eudrilus eugeniae specimen from a vermicompost site.
Transcriptome profiling of human monocytes from accute phase and convalescent patients infected with Plasmodium vivax malaria
<p> RNA sequencing raw data of monocytes obtained from five patients in acute phase of <em>Plasmodium vivax </em>malaria and 45 days after treatment. Individuals from Rondônia (Brazil) were between 18 and 60 years and attended to the exclusion criteria: chronic inflammatory or infectious diseases, pregnancy, and breastfeeding. Diagnostic of malaria by clinical symptoms and thick blood smear was also confirmed by qPCR. According to the recommended protocol of the Brazilian Ministry of Health patients were treated with chloroquine associated to primaquine. Protocols used were approved by the Ethical Committees on Human Experimentation from Instituto Rene ́ Rachou, Fiocruz, and National Ethical Council (CAAE: 59902816.7.0000.5091). </p> <p> Sequencing was performed in a NextSeq 500 system using the NextSeq 500/550 High Output Kit v2.5 for 75 cycles (both from Illumina). Before sequencing total mRNA samples were prepared using TruSeq Stranded mRNA Kit (Illumina) according to the manufacturer’s protocol. Files are in FASTQ format, just as they were downloaded from Illumina's cloud environment (BaseSpace) right after sequencing. Files are identified by patient number (1 to 5) and health condition: acute phase of the disease or convalescent (45 days after treatment).</p>
Gradients1-KOK1606 Hyperpro Profiles
<p>The Satlantic (now Seabird) HyperPro is an in-situ free-fall profiling unit designed to measure the apparent optical properties of the ocean with concurrent measurements of temperature, salinity, chlorophyll and dissolved matter fluorescence, and optical backscattering (a proxy for total particle load). The unit is equipped with one up-looking and one down-looking hyperspectral (350-800nm) radiometer with 10 ± 0.3 nm resolution, a WET Labs ECO-Puck Triplet, and temperature, pressure, and conductivity sensors. It also incorporates a ship-mounted surface (air) hyperspectral radiometer. These data are collated from the vertical profiles conducted during the Gradients I cruise.</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Supporting data for "Morphological profiling by high-throughput single-cell biophysical fractometry"
<p>No specific description. For more information, please contact: iriszqzh@connect.hku.hk.</p>
Profiled Likelihoods from A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data
<p>This repository contains the profiled likelihood ratios calculated in "A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data" [arXiv:2212.07435] for the unmasked analysis. Profiled likelihood ratios are provided for each of the 30 annuli considered in the work joined over the top three quartiles of data in energy dispersion.</p> <p>The data are provided in an hdf5 archive containing 532 datasets. Line rest energies in units of GeV are provided in the `Line_Energy` dataset, which is a length 531 1-D array. Each of the remaining 531 datasets contains a 3-D array of shape (30, 2, 1001) corresponding to the profiled likelihood ratio as a function of line intensity, in units of counts/cm<sup>2</sup>/s/sr. The log of the profiled likelihood ratio for the i<sup>th</sup> line rest energy in the j<sup>th</sup> annulus may be accessed by:</p> <pre><code class="language-python">archive = h5py.File('./Archive.h5', 'r') line_energy = archive['Line_Energy'][i] data = archive['Line_' + str(i)][j] line_intensity = data[0] log_profiled_likelihood_ratio = data[1] archive.close()</code></pre> <p>For details, see <a href="https://arxiv.org/abs/2212.07435">https://arxiv.org/abs/2212.07435</a>. </p>
Datasets for the manuscript "Metabolomic and Sphingolipidomic Profiling of Human Hepatoma Cells Exposed to Widely Used Pharmaceuticals"
<p>See experimental details on the main text of the manuscript "Metabolomic and Sphingolipidomic Profiling of Human Hepatoma Cells Exposed to Widely Used Pharmaceuticals" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jpba.2024.116378" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jpba.2024.116378</span></span></a></p> <h2><strong>Data description</strong></h2> <p>This study investigates the impact of three commonly used pharmaceuticals (amoxicillin, carbamazepine, and trazodone) on human liver cells. To mimic real-world conditions, liver cells were encapsulated in spheroids and exposed to various concentrations of these drugs for 24 hours. The study employs metabolomic and sphingolipid analyses to identify metabolic changes induced by drug exposure.</p> <div></div> <p></p> <div> <div> <div> <h3>LC-MS/MS Method for Sphingolipid Analysis</h3> <p>Targeted sphingolipid analysis was conducted using a Waters ACQUITY UPLC System coupled to a Waters Xevo TQ-S system equipped with an Electrospray Ion Source (ESI) and ScanWave™ collision cell technology, operating in positive mode [16]. Sphingolipids were quantified using a Zorbax Rapid Resolution RRHD C18 Column (80 Å, 1.8 µm, 2.1 mm × 100 mm).</p> <h3>LC-HRMS Method for Semi-Targeted Metabolomic Analysis</h3> <p>LC-HRMS analysis was performed on an Agilent 1290 Affinity II HPLC system coupled to an Agilent 6550 iFunnel QTOF mass spectrometer equipped with a dual AJS electrospray ionization source operating in both positive and negative modes. Polar metabolite screening was conducted using a SeQuant® ZIC®-pHILIC 5 µm polymer 100 × 2.1 mm column.</p> </div> </div> </div> <p> </p> <h2><strong>Funding</strong></h2> <p>The research leading to these results has received funding from the Spanish Ministry of Science and Innovation MCIN/AEI/ 10.13039/501100011033, Grants CTQ2017-82598-P and CEX2018-000794-S. The authors also want to grant support from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). Miriam Pérez-Cova acknowledges a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP), and Post-graduate department from CSIC for the funding of the research stay in Karolinska Institute, <em>via</em> the award to best outreach video in the YoInvestigoYosoyCsic contest, 2019 edition. </p>
FIG. 11 in Fungal biodiversity profiles 111-120
FIG. 11. — Inocybe leucophaea Eyssart. & Buyck, sp. nov. Photo: B. Buyck.
FIG. 7 in Fungal biodiversity profiles 111-120
FIG. 7. — Inocybe hebes Eyssart. & Buyck, sp. nov. Photo: B. Buyck.
FIG. 9 in Fungal biodiversity profiles 111-120
FIG. 9. — Inocybe media Eyssart. & Buyck, sp. nov. Photo: B. Buyck.
Systematic exploration of mitochondrial DNA- and RNA-protein complexes by complexome profiling (datasets)
<p>Supplemental data files that include the results of complexome profiling experiments from the manuscript entitled "<strong>Let’s make it clear: Systematic exploration of mitochondrial DNA- and RNA-protein complexes by complexome profiling" by Potter et al</strong>. </p> <p>Supplemental Data 1: comparison of mitochondrial complexomes obtained with either Blue Native (BNE) or high resolution Clear Native (hrCNE) gel electrophoresis. </p> <p>Supplemental Data 2: comparison of mitochondrial complexomes obtained with hrCNE from samples supplemented or not with DNase I. </p> <p>Supplemental Data 3: comparison of mitochondrial complexomes obtained with hrCNE from samples supplemented with only DNase I or a combination of DNase I and RNase A. </p> <p>Supplemental Data 4: comparison of mitochondrial complexomes obtained with hrCNE from control cells, cells treated with ethidium bromide and cells recovering after ethidium bromide treatment.</p> <p>Supplemental Data 5: results of LFQ-based analysis of profiles obtained from samples supplemented or not with DNase I.</p> <p>Supplemental Data 6: individual profiles (replicates), alignment and mass calibration results for the presented data.</p>
Synthetic velocity profiles for simulations of blood flow in the aorta
<p>Synthetic dataset of aortic velocity profiles, suitable to be used for numerical simulations of blood flow.</p> <p>Please refer to the profile number ID when using it.</p>
Pulse Profile Modeling of Thermonuclear Burst Oscillations I: The Effect of Neglecting Variability
<p>Pulse Profile Modeling of Thermonuclear Burst Oscillations I: The Effect of Neglecting Variability</p>
Comparing Finnish universities' publication profiles using multidimensional field-normalized indicators - dataset
<p>This dataset from the VIRTA Publication Information Service consists of the metadata of 241,575 publications of Finnish universities (publication years 2016–2021) merged from yearly datasets downloaded from <a href="https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot">https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot</a>.</p> <p>The dataset contains following information:</p> <ul> <li>Organisation: name of the university</li> <li>Publication year: the year of publication</li> <li>Subfield: one of 66 fields of science based on Statistics Finland field of science classification (in Finnish), see the classification in English: <a href="https://www2.stat.fi/en/luokitukset/tieteenala/">https://www2.stat.fi/en/luokitukset/tieteenala/</a></li> <li>Peer-reviewed: 1=peer-reviewed publications, 0=not peer-reviewed publications</li> <li>Science communication: 1=publications aimed at professional and general audiences, 0=peer-reviewed and not peer-reviewed publications aimed at academic audience.</li> <li>Bibliodiversity: 1=peer-reviewed book publications (chapters, monographs and edited volumes) and conference articles, 0=peer-reviewed journal articles.</li> <li>Multilingualism: share of peer-reviewed publications in languages other than English (Finnish, Swedish and other languages).</li> <li>Domestic publishing: 1=peer-reviewed publications in journals and books published in Finland, 0=peer-reviewed publications in journals and books published outside Finland.</li> <li>Domestic collaboration: 1=peer-reviewed publications with co-authors from more than one Finnish university, 0=peer-reviewed publications without co-authors from more than one Finnish university.</li> <li>International collaboration: 1=share of peer-reviewed publications with co-authors affiliated with foreign institutions, 0=share of peer-reviewed publications without co-authors affiliated with foreign institutions.</li> <li>Research performance: 1=peer-reviewed outputs in JUFO levels 2 (“leading”) and 3 (“top”) publication channels, 0=peer-reviewed outputs in JUFO levels 1 (“basic”) and 0 (“other”) publication channels.</li> <li>Open access: 1=peer-reviewed open access publications, including gold, hybrid and green OA, 0=peer-reviewed closed publications.</li> </ul>
microRNA profiling of amniotic fluid samples from CAKUT fetuses
<p><strong>Summary</strong></p> <p>Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood end-stage renal disease and a significant cause of chronic kidney disease in adults. Genetic and environmental factors have been shown to influence CAKUT development, but the known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT through multi-omics analysis, including miRNome, peptidome and proteome data. The peptidome and proteome data of the corresponding samples were previously published [pmid: 32750455, pmid: 33987838].</p> <p><strong>Study design</strong></p> <p>Amniotic fluid samples were collected in a prospective multicenter observational study focusing on fetal bilateral CAKUT as part of a <a title="clinical trial" href="https://clinicaltrials.gov/ct2/show/NCT02675686">clinical trial</a>. In addition, 21 samples were collected from non-CAKUT individuals. The severity was defined based on the renal status after 2 years of postnatal clinical follow-up.</p> <p>The total RNA was isolated using the Agilent RNA 6000 Pico kit protocol (5067-1513) and microRNAs were profiled using Agilent microRNA slides (Sanger miRBase release 21). The samples were labelled and hybridized according to the Agilent's microRNA Complete Labeling and Hybridization Kit protocol (5190-0456), followed by Spike-ins with the Agilent's microRNA Spike-In Kit protocol (5190-1934) and analyzed using Aligent's High-Resolution Microarray Scanner GS2505_C. Features were called using the Agilent Feature Extraction software (version 11.0.1.1) and sample intensities were normalized using quantile normalization (RMA).</p>
NCEDs drive Rising but not Peaking abscisic acid profiles in diverging conifer species
<p>Datasets supporting experimental results:</p> <ul> <li><strong>MWP data</strong>: Soil water content and Midday water potential readings of experimental trees across three drought trials</li> <li><strong>ABA data</strong>: Abscisic acid quantitation of experimental trees at selected time points, across three drought trials (species re-tested in trial 2 not included).</li> <li><strong>Gene expression data</strong>: transcript levels of target ABA-related genes, normalised versus controls and housekeeping genes (first drought trial).</li> </ul>
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.