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Tumor multi-omics profiles and clinical information employed in TMO-Net research
<p>This dataset includes tumor multi-omics profiles used in TMO-Net research. The preprocessed TCGA pan-cancer multi-omics were employed during pre-training stage of TMO-Net. The metabric multi-omics dataset, metastatistic tumor multi-omics dataset, PDX cell line multi-omics dataset, GDSC cell line multi-omice dataset, CPTAC cancer multi-omics dataset and metastatic dataset were used for downstream tasks and analysis.</p>
Simulated images and profiles
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Gene expression profile at both bulk and single cell level for heterogeneous cell type mixtures of A375, HAP1, THP1, HEK239FT, K562, and A549
<p>We used poly-A bulk RNA sequencing and single-cell RNA sequencing using mixtures of well-characterized purified cell lines to better understand measurement noise between technologies. A375, HAP1, THP1, HEK239FT, K562, and A549 cells were mixed in various proportions to generate four in vivo cell mixtures that mimic tissues. Mixtures of 6, 4, 2, and 1 of the cell types are respectively denoted 6ct, 4ct, 2ct, and 1ct. In the included zip, directories are labeled using this notation, followed by a list of cell types in the mixture after an underscore. In each directory, bulk and single cell count data is included.</p>
Figure 1 in Comparison of proteomic profiles of the phaeophyte Saccharina japonica thalli proximal to and beneath the front of epiphytic hydrozoan colonies against healthy tissue
Figure 1: Saccharina japonica thallus sections defined as thallus tissue proximal to hydrozoan colonies and tissue at the colony front. The proximal tissues were collected from the 1-cm zone outside the boundary of the colony. The colony-front tissues were obtained from the 1-cm zone under the newly formed front of the colony after removing the hydrozoans. Inset photo shows hydrozoans on the brown alga S. japonica.
Figure 4 in Comparison of proteomic profiles of the phaeophyte Saccharina japonica thalli proximal to and beneath the front of epiphytic hydrozoan colonies against healthy tissue
Figure 4: Sections of two-dimensional gels showing the proteins (arrows) that decreased during hydrozoan colonization. The separated proteins were visualized by silver staining. (A) Distal healthy thallus tissue. (B) Thallus tissue proximal to hydrozoan colonies. (C) Thallus tissue at the colony front.
Figure 3 in Comparison of proteomic profiles of the phaeophyte Saccharina japonica thalli proximal to and beneath the front of epiphytic hydrozoan colonies against healthy tissue
Figure 3: Sections of two-dimensional gels showing the proteins (arrows) that increased most in the thallus tissue proximal to hydrozoan colonies or in the thallus at the colony front, but were not detected in the distal healthy thallus tissue. The separated proteins were visualized by silver staining. (A) Distal healthy thallus tissue. (B) Thallus tissue proximal to hydrozoan colonies. (C) Thallus tissue at the colony front.
Figure 5 in Influences of hydrozoan colonization on proteomic profiles of the brown alga Saccharina japonica
Figure 5: A close-up view of 2-dimensional electrophoresis gels showing the identified down-regulated proteins (indicated by arrows) altered by hydrozoan colonization. (A) Healthy tissues. (B) Hydrozoan-colonized tissues.
Figure 1 in Influences of hydrozoan colonization on proteomic profiles of the brown alga Saccharina japonica
Figure 1: Two-dimensional gel electrophoresis profiles of late-harvested Saccharina japonica. (A) Healthy tissues. (B) Hydrozoan-colonized tissues. The separated proteins were visualized by silver staining.
Figure 3 in Influences of hydrozoan colonization on proteomic profiles of the brown alga Saccharina japonica
Figure 3: A close-up view of 2-dimensional electrophoresis gels showing the identified up-regulated proteins (indicated by arrows) altered by hydrozoan colonization. (A) Healthy tissues. (B) Hydrozoan-colonized tissues.
Figure 4 in Influences of hydrozoan colonization on proteomic profiles of the brown alga Saccharina japonica
Figure 4: A close-up view of 2-dimensional electrophoresis gels showing the identified down-regulated proteins (indicated by arrows) found mostly in healthy tissues but rare in hydrozoancolonized tissues. (A) Healthy tissues. (B) Hydrozoan-colonized tissues.
Figure 2 in Influences of hydrozoan colonization on proteomic profiles of the brown alga Saccharina japonica
Figure 2: A close-up view of 2-dimensional electrophoresis gels showing the identified up-regulated proteins (indicated by arrows) found mostly in hydrozoan-colonized tissues but rare in healthy tissues. (A) Healthy tissues. (B) Hydrozoan-colonized tissues.
Dataset - Flux Profile Optimization of a High-Flux Solar Simulator Using the Trust-Region Reflective Method
<p>This dataset accompanies</p> <p>DOI: 10.1080/14786451.2024.2355651</p> <p>Title: Flux Profile Optimization of a High-Flux Solar Simulator Using the Trust-Region Reflective Method</p> <p>Authors: Mohammed Hamid Hussain</p> <p>Journal: International Journal of Sustainable Energy</p> <p> </p>
Supplementary Data from manuscript entitled: Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef
<p>The data used in the manuscript "Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef" is available. It contains information from different instruments on board the research aircraft. The manuscript describes the instrumentation details. There is a read_me file in each folder with further information.</p>
Spatial and seasonal fluctuations in fresh submarine groundwater discharge revealed by marine continuous resistivity profiling
<p>MCRP, Piezometric level Seawater, Radium isotope activities, Seawater salinity profiles (Mataró, Spain)</p>
SSR and SNP profiles obtained for 40 non-redundant grapevine genotypes found in the living collection of Ain Taoujdate (Morocco)
<p>This dataset includes the genetic profiles (13 SSR and 240 SNP markers) of 40 grapevine genotypes identified in the living collection of Ain Taoujdate (Morocco)</p>
Prevalence, resistance profiles and factors associated with skin and soft-tissue infections at Jinja regional referral hospital: A retrospective study
<p>Skin and soft-tissue infections (SSTI) are common cases of hospital-acquired infections with aetiologic agents exhibiting antimicrobial resistance (AMR). We determined the prevalence, proportion of laboratory-investigated cases, AMR-profiles, and factors associated with SSTI and multi-drug resistance (MDR). This study was based on archived data of patients suspected of SSTI from 2019-2021 at Jinja Regional Referral Hospital. The analysis involved 268 randomly selected patient reports. Prevalence of SSTI was 66.4%. Laboratory-investigated cases were 14.11%. <em>Staphylococcus aureus </em>(n=51) was the most isolated organism. MDR pathogens explained 47% of infections. Methicillin-resistant <em>S. aureus</em> was up to 44%. In addition, 61% of Gram-negatives had the potential to produce extended-spectrum beta-lactamases, while 27% were non-susceptible to carbapenems. Ward of admission was significantly associated with infection (aPR=1.78, 95% CI: 1.003-3.18, p-value=0.04). Age category <u>(</u>19-35) was an independent predictor for MDR infections (aPR=2.30, 95%CI:1.02-5.23, p-value=0.04). The prevalence is relatively high with MDR pathogens responsible for almost half of the infections. Routine use of culture and sensitivity testing should be done for proper infection management. Gentamicin and ciprofloxacin can be considered for empirical management of emergency SSTI suspected of <em>S. aureus</em>. Recognizing SSTI under the Global Antimicrobial resistance Surveillance System (GLASS) would lead to improved preparedness and response to AMR.</p>
Functional profiles of 14 strains of Salinicola using EggNOG online service
<p>Functional profiles of 14 Salinicola strains using the EggNOG online service (http://eggnog-mapper.embl.de).</p> <p>The zip file contains tsv and xlsx files generated using eggNOG to classify the gene-encoding proteins according to the next EggNOG categories:</p> <p>*Information Storage and Processing*<br>A) RNA processing and modification <br>B) Chromatin structure and dynamics<br>J) Translation, ribosomal structure and biogenesis<br>K) Transcription<br>L) Replication, recombination and repair <br>*Cellular processes and signaling*<br>D) Cell cycle control, cell division, chromosome partitioning <br>M) Cell wall/membrane/envelope biogenesis <br>N) Cell motility <br>O) Posttranslational modification, protein turnover, chaperones <br>T) Signal transduction mechanisms <br>U) Intracellular trafficking, secretion, and vesicular transport <br>V) Defense mechanisms <br>*Metabolism*<br>C) Energy production and conversion <br>E) Amino acid transport and metabolism <br>F) Nucleotide transport and metabolism<br>G) Carbohydrate transport and metabolism <br>H) Coenzyme transport and metabolism <br>I) Lipid transport and metabolism <br>P) Inorganic ion transport and metabolism <br>Q) Secondary metabolites biosynthesis, transport and catabolism <br>*Poorly characterized<br>S) Function unknown </p> <div> <pre>-. .-. .-. .-. .-. .-. . ||\|||\ /|||\|||\ /|||\|||\ /| |/ \|||\|||/ \|||\|||/ \|||\|| ~ `-~ `-` `-~ `-` `-~ `- </pre> </div> <p>Profiles generated on 3 June 2024</p> <p> </p>
Figure 2 in Comparison of proteomic profiles of the phaeophyte Saccharina japonica thalli proximal to and beneath the front of epiphytic hydrozoan colonies against healthy tissue
Figure 2: Two-dimensional gel electrophoresis profiles of lateharvested Saccharina japonica. (A) Distal healthy thallus tissue. (B) Thallus tissue proximal to hydrozoan colonies. (C) Thallus tissue at the colony front. The separated proteins were visualized by silver staining. Numbers attached to the arrows refer to the spot numbers listed in Table 1.
In silico mock communities for evaluation of taxonomic profilers across eukaryotes in the human microbiome
<p><em>In silico </em>mock communities generated with CAMISIM for benchmarking the performance of taxonomic profilers across prokaryotic (50 communities), eukaryotic (30 communities), and viral communities (10 communities) of the human microbiome. Metagenomes were generated using CAMISIM (Fritz et al., 2019), which simulates 2.1 Gb of Illumina 2 ×150 bp paired end reads with the default HiSeq 2500 error profile and a mean insert size of 200 bp.</p> <p><strong>Eukaryotic communities<br></strong>30 eukaryotic <em>in silico</em> metagenomes comprising up to 200 randomly sampled genomes from a set of 113 eukaryotic species (See Supplementary Table 2 from the paper) corresponding to the eukaryotic species within both CHAMP and MetaPhlAn 4 (Blanco-Míguez et al., 2023) databasess.</p> <p><strong>Prokaryotic and viral communities</strong></p> <p>In silico data for prokaryotic and viral communities from the human microbiome can be found here: <a href="https://doi.org/10.5281/zenodo.10777404">doi: 10.5281/zenodo.10777404</a></p> <p><strong>References</strong></p> <p>Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., et al. (2023). Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. <em>Nature Biotechnology 2023 41:11</em> 41, 1633–1644. doi: 10.1038/s41587-023-01688-w</p> <p>Fritz, A., Hofmann, P., Majda, S., Dahms, E., Dröge, J., Fiedler, J., et al. (2019). CAMISIM: Simulating metagenomes and microbial communities. <em>Microbiome</em> 7, 1–12. doi: 10.1186/S40168-019-0633-6/FIGURES/5</p>
The processed atmospheric wind profiles dataset in near space from ERA5 (20-50km)
<h2>experimental data</h2>
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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.