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Dataset related to the article "Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC"
<p>This record contains original data used in the article "Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC" to develop a linear support vector machine (SVM) classifier to predict chromosomal instability. </p> <p>We retrospectively evaluated the results of plasma NGS analysis performed at our Institution by using the AVENIO ctDNA Expanded Kit, a panel of 77 genes, which detects the major classes of genetic alterations. Binary classification, into “stable” (SCP) or “unstable” (UCP) chromosomal profiles, was initially performed by visual inspection of individual CNV alteration profiles by two independent professionals of our group. Then we decided to implement a support vector machine (SVM) classifier to automatically classify CNV profiles as SCP or UCP, beyond operators’ experience. We considered the segmented log2 ratios (.cns) files provided by the CNV kit software and computed three features (Segments, Size, Chromosomes). An alteration (“occurrence of instability”) in the CNV profile was defined each time we found a DNA segment of any size with absolute value of the log2 copy ratio exceeding a fixed cut-off. Two different cut-off values on log2 copy ratio were examined: 0.1 and 0.2. Once the cut-off was defined, three features were considered as covariates in the SVM classifier: 1) number of altered segments (Segments), 2) total length of altered regions (Size) and 3) number of affected chromosomes (Chromosomes).</p> <p>The “dataset_0.1.txt” and “dataset_0.2.txt” files are the original data matrices obtained by considering a cut-off of 0.1 and 0.2, respectively, on the absolute value of the log2 copy ratio.</p> <p>Rows represent available samples in our study (n=177). Columns contain the following variables: anonymized sample IDs (Sample), the class, “stable” or “unstable”, as assigned by two independent professionals of our group (Class), the corresponding binary label (Label: 0 for “stable”, 1 for “unstable”), the three features used as covariates in the SVM classifier and computed as described above (Segments, Size, Chromosomes).</p> <p><br>For the detailed results of our work, please refer to the full article.</p>
Integrating pigment and fatty acid profiles for enhanced estimation of seston community composition
<p>Climate change, nutrition pollution, and land use alterations influence the primary production of lakes. While light-microscopy counting remains the standard for estimating phytoplankton community composition, its expense and time-consuming nature necessitate cost-effective alternatives for seston analysis. Furthermore, estimating the contribution of seston constituents other than primary producers, or non-algal particles, is not possible with light-microscopy counting. Biotracer approach using computational methods and chemotaxonomic biomarkers such as carotenoid pigments and fatty acids have been utilised as an alternative in seston analysis when species-level taxonomy is not required. However, a comprehensive testing of how well carotenoid and fatty acids can be utilised in estimating a wide range of seston phytoplankton communities using different estimation methods is lacking. To assess the accuracy of a suite of state-of-the-art biotracer-based computational methods, namely CHEMTAX, FASTAR (Fatty Acid Source-Tracking Algorithm in R), MixSIAR, and QFASA (Quantitative Fatty Acid Signature Analysis), lake water samples were collected in 2016, 2018, 2019, 2020, and 2021 for seston composition analysis in a boreal eutrophic lake with light-microscopy counting serving as the reference for seston composition. Absolute errors between the biotracer-based estimates were calculated to evaluate method performance. A small laboratory experiment to assess the reliability of estimating the contribution of non-algal particles using the computational methods with fatty acids was also conducted. The closest alignment to light-microscopy counting in terms of absolute error was achieved when both carotenoids and fatty acids were utilised together in the QFASA method. For CHEMTAX, FASTAR, and MixSIAR utilising carotenoids alone produced the closest results. Additionally, the estimation methods accurately assessed the proportion of non-algal particles in the seston when utilising fatty acid profiles, a capability not possible with light-microscopy counting. Our findings demonstrate that the biotracer approach provides a viable and cost-effective alternative to light-microscopy counting when group-level information of phytoplankton community composition suffices. Furthermore, we show that non-algal particles can be effectively estimated together with phytoplankton when utilising fatty acids.</p>
Figure 1. Kaempferol-3-O in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 1. Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside – tiliroside.
Research Compendium for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive is the Reproducible Research Compendium for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques.</p> <p>This compendium includes all files related to MARGE associated with the manuscript.</p> <p>NN model files are split into smaller files for convenience, given their sizes. To recombine the files, do, e.g., <br> cat cnn_weights_NH-LW.h5* > cnn_weights_NH-LW.h5</p> <p>User interested in running MARGE will need to clone the GitHub repo (https://github.com/exosports/MARGE), apply the patch file to checksum fc95b3c, organize the relevant files into directories as listed in the configuration files (Zenodo does not support organizing files into directory structures) and calculate the number of training, validation, and test cases to be stored in the relevant input file specified in the configuration file. MARGE is under the Reproducible Research Software License (https://planets.ucf.edu/resources/reproducible-research/software-license/). For more details on MARGE, see the User Manual on GitHub.</p>
Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests
<p>This dataset supports the analysis about <em>Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests</em></p>
Hematological and biochemical profiles, infection and habitat quality in an urban rat population
<p>Dataset employed in the analysis of "Hematological and biochemical profiles, infection and habitat quality in an urban rat population". For details in sampling methodology, see article of same name.</p> <p>Composed of three dataset files, with codebooks associated:</p> <ul> <li>animal_data: morphological and parasitological data of captured individuals</li> <li>hematological_data: data on hematological parameters of captured individuals</li> <li>biochemical_data: data on biochemical markers of captured individuals</li> </ul> <p>All individuals are identified by an individual code (rat_ID), and can be cross-linked through the bases.</p> <p>Files are composed of two sheets</p> <p>-data sheet: original data, with regular labels</p> <p>-codebook: comprehensive codebook for the data sheet.</p>
Soil carbon maintained by perennial grasslands but lost in field crop systems over 30 years in a temperate Mollisol according to longitudinal, compaction-corrected, full-soil profile analysis
<p>To mitigate climate change, some seek to store carbon from the atmosphere in agricultural soils. However, our understanding of how agriculture affects soil organic carbon (SOC) is muddied by studies 1) lacking longitudinal data, 2) ignoring bulk density changes, or 3) sampling only surface soils. To better understand SOC trends, here we measured changes over 30 years in density-corrected, full-soil-depth (90 cm) SOC stocks under 6 cropping systems and a restored prairie in a Mollisol of southern Wisconsin, USA. Cash-grain systems and alfalfa-based systems lost SOC. Prairie and rotationally-grazed pasture maintained SOC. Average SOC losses for cash-grain and alfalfa-based systems were -0.82 (±0.12) and -0.64 (±0.17) Mg C ha<sup>-1</sup> yr<sup>-1</sup>, respectively. Sensitivity analysis showed that incomplete methodologies overestimated SOC improvements. Our findings using more comprehensive methods demonstrate the inadequacy of row-crop systems and the need for well-managed grasslands to protect SOC in productive agricultural soils of the Upper Midwest USA.</p>
FIG. 11 in Fungal Biodiversity Profiles 101-110
FIG. 11. — Inosperma submaculatum Eyssart. & Buyck, sp. nov. (PC0088757). Photo: B. Buyck.
FIG. 9 in Fungal Biodiversity Profiles 101-110
FIG. 9. — Inosperma boeticum Eyssart.& Buyck, sp. nov. (holotype,PC0088774). Photo: B Buyck.
In silico mock communities for evaluation of taxonomic profilers across prokaryotes and viruses
<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. To assess profiling performance for a range of sequencing depths, the 50 <em>in silico</em> metagenomes were also rarefied with seqtk (-s100) to sequencing depths of 20, 5, 2, 1, 0.5, 0.25 and 0.1 million read pairs. Counts are provided for rarefied metagenomes.</p> <p><strong>Prokaryotic communities<br></strong>For prokaryotic benchmarking, 10 body site-representative prokaryotic metagenomes were simulated for each of the following five body sites: adult gut, infant gut, oral, skin, and vagina. Genome accession ids for prokaryotic species found in each human body site were identified from published literature (Bäckhed et al., 2015; Proctor et al., 2019; Saheb Kashaf et al., 2021). </p> <p>Adult Gut: pro_gut_adult.zip<br>Infant Gut: pro_gut_infant.zip<br>Oral: pro_oral.zip<br>Skin: pro_skin_1.zip, pro_skin_2.zip, pro_skin_3.zip<br>Vaginal: pro_vaginal.zip</p> <p>Downsized counts: </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) databases.</p> <p>Eukaryotic data is deposited here: <a href="https://doi.org/10.5281/zenodo.12090449" target="_blank" rel="noopener">doi: 10.5281/zenodo.12090449</a></p> <p><strong>Viral communities</strong><br>10 viral communities were simulated with 95% of the reads from bacteria and 5% of the reads originating from phages. Each community consisted of 200 randomly selected bacterial genomes from GTDB with species-level annotation and 200 viral genomes from the Gut Phage Database (GPD, Camarillo-Guerrero et al., 2021). </p> <p>Counts: phage_communities_counts.zip<br>FastQ, forward reads: camisimu_[1-10].fq.1.gz<br>FastQ, reverse reads: camisimu_[1-10].fq.2.gz</p> <p><strong>References</strong></p> <p>Bäckhed, F., Roswall, J., Peng, Y., Feng, Q., Jia, H., Kovatcheva-Datchary, P., et al. (2015). Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. <em>Cell Host Microbe</em> 17, 690–703. doi: 10.1016/J.CHOM.2015.04.004</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>Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D., and Lawley, T. D. (2021). Massive expansion of human gut bacteriophage diversity. <em>Cell</em> 184, 1098. doi: 10.1016/J.CELL.2021.01.029</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> <p>Proctor, L. (2019). Priorities for the next 10 years of human microbiome research. <em>Nature 2021 569:7758</em> 569, 623–625. doi: 10.1038/d41586-019-01654-0</p> <p>Saheb Kashaf, S., Proctor, D. M., Deming, C., Saary, P., Hölzer, M., Mullikin, J., et al. (2021). Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. <em>Nature Microbiology 2021 7:1</em> 7, 169–179. doi: 10.1038/s41564-021-01011-w</p>
Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation
<p>Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 72 hr after ozone, while pathways involved in cell cycle regulation were upregulated at 24 hr and intracellular metabolism at 72 hr. An interaction network analysis identified tumor suppressor 53 (TP53), E2F family of transcription factors (E2Fs), Cyclin Dependent Kinase Inhibitor 1A (CDKN1a/p21), and Cyclin D1 (CCND1) as upstream regulators of cell cycle pathways at 24 hr and TP53, nuclear receptor subfamily 4 group a member 1 (NR4A1/Nur77), and estrogen receptor alpha (ESR1/ERα) as central upstream regulators of mitochondrial respiration pathways at 72 hr. These results highlight the complex interaction between cell cycle, intracellular metabolism, and macrophage activation which may be important in the initiation and resolution of inflammation following ozone exposure.</p>
Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalised therapy
<div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here we show how a personalised organotypic 'avatar' created from resected tissue, allows spatial and temporal reporting on a complete in situ tumour microenvironment, and mirrors clinical responses. Our perfusion culture method extends tumour slice viability, maintaining stable tumour content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighbourhoods and potential for immunotherapy. We employed avatars to assess the impact of a pre-clinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumour re-differentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identified a patient avatar-predicable response from clinical follow-up. Thus, avatars provide valuable information for the syngeneic testing of novel therapeutics and a truly personalised therapeutic assessment platform for patients.</p> </div> </div> </div>
Fig 4.A in Study of haematology profile & histopathological changes in di-ammonium phosphate induced climbing perch, Anabas testudineus (Bloch.)
Fig 4.A: Photomicrograph of the normal liver of control fish, Anabas testudineus. H. & E., 100X
Resolving single-cell expression profiles by pseudo-temporal integration of transcriptomic and proteomic datasets.
<p>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.</p>
TwitterGAN dataset: Fake Twitter accounts using GAN-generated faces as profiles
<p>We release the TwitterGAN dataset, which contains 1,420 fake Twitter accounts using GAN-generated faces as their profiles.</p> <p>See https://github.com/osome-iu/fake_gan_accounts for details.</p>
Database of simulated pulse profiles from X-ray accreting pulsar
<p>This database is a parquet file that stores a list of dictionary with the simulated pulse profiles from an X-ray accreting pulsar. Every dictionary contains 7 paframeters defining the pulsar and the associated pulse profile. It is highly recommended to check the GitHub repository containing instructions on how to read and use the data stored. </p> <p><a href="https://github.com/SimoneGalla/X-ray-accreting-pulsar/tree/main/pulse-Profile-X-ray-pulsars">X-ray-accreting-pulsar/pulse-Profile-X-ray-pulsars at main · SimoneGalla/X-ray-accreting-pulsar (github.com)</a></p>
The effect of extracellular vesicles derived from oral squamous cell carcinoma on the metabolic profile of oral fibroblasts
<p><span>Oral cancer is one of the most common forms of head and neck cancers. Oral squamous cell carcinoma (OSCC) accounts for more than 90% of the oral malignancies. The molecular pathogenesis of OSCC is complex as it involves altered expression of specific genes and proteins, but also comprises changes in metabolic processes. It is suggested that extracellular vesicles (EVs) released by cancer cells may contribute to cancer development and metastasis by recruiting and changing phenotype of normal cells that surround the tumor. The aim of the project was to characterize the effect of OSCC EVs on the metabolic profile of normal oral fibroblasts (NOFs). Targeted </span><span>liquid chromatography-mass spectrometry metabolic profiling was performed on control cells and NOFs exposed to OSCC EVs for 24 and 48 h. Analysis of detected metabolites revealed that OSCC EVs affected NOFs the most after 24 h of exposure. Among metabolites that were significantly altered at 24 h, </span><span>pyruvate, ATP, UTP, coenzyme A, and dihydroxyacetone phosphate were upregulated, while fatty acids such as nervonic acid, linoleate, oleate, palmitoleic acid, and docosahexaenoic acid were downregulated. These findings were supported by Western blotting of pyruvate kinase M2 (PKM2). The metabolic pathways of glycolysis, </span><span>citric acid cycle, and </span><span>amino acid metabolism were enriched, suggesting that OSCC EVs cause phenotype switch in NOFs that may contribute to </span><span>acquiring</span><span> a pro-tumorigenic phenotype.</span></p>
In-Plume Profiling Methodology: Ensuring Firefighter Safety During Pyroconvective Extreme Wildfire Events
<p>dataset for the paper: <span>In-Plume Profiling Methodology: Ensuring Firefighter Safety During Pyroconvective Extreme Wildfire Events</span></p> <p> </p> <p><span>It includes the radiosonde flight files used in the methodology</span></p> <p><span>It includes the videos of radiosondes launching as complementary materials</span></p>
Nicosia, Cyprus. Bedesten, apse window, north aisle, moulding profile.
<p>Nicosia, Cyprus. Bedesten, apse window, north aisle, moulding profile. As documented 1981.</p>
Nicosia, Cyprus. Bedesten, east end, picina, moulding profile.
<p>Nicosia, Cyprus. Bedesten, east end, picina, moulding profile. As documented 1981.</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.