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120 results for “Commonality analysis”
Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions
<p>This data set corresponds to the paper: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions [1] (Experiment: Comprehensive reporting).</p> <p>The key research questions corresponding to this data set were:</p> <p>RQ1: What is the role of challenges for the field of biomedical image analysis (e.g. How many challenges conducted to date? In which fields? For which algorithm categories? Based on which modalities?)</p> <p>RQ2: What is common practice related to challenge design (e.g. choice of metric(s) and ranking methods, number of training/test images, annotation practice etc.)? Are there common standards?</p> <p>RQ3: Does common practice related to challenge reporting allow for reproducibility and adequate interpretation of results?</p> <p>To address these research questions, we aimed to capture all biomedical image analysis challenges that have been conducted up to 2016. To acquire the data, we analyzed the websites hosting/representing biomedical image analysis challenges, namely grand-challenge.org, dreamchallenges.org and kaggle.com as well as websites of main conferences in the field of biomedical image analysis, namely Medical Image Computing and Computer Assisted Intervention (MICCAI), International Symposium on Biomedical Imaging (ISBI), International Society for Optics and Photonics (SPIE) Medical Imaging, Cross Language Evaluation Forum (CLEF), International Conference on Pattern Recognition (ICPR), The American Association of Physicists in Medicine (AAPM), the Single Molecule Localization Microscopy Symposium (SMLMS) and the BioImage Informatics Conference (BII). This yielded a list of 150 challenges with 549 tasks.</p> <p>Next, a tool for instantiating the challenge parameter list introduced in [1] was used by some of the authors (engineers and medical student) to formalize all challenges that met our inclusion criteria as follows: (1) Initially, each challenge was independently formalized by two different observers. (2) The formalization results were automatically compared. In ambiguous cases, when the observers could not agree on the instantiation of a parameter - a third observer was consulted, and a decision was made. When refinements to the parameter list were made, the process was repeated for missing values. Based on the formalized challenge data set, a descriptive statistical analysis was performed to characterize common practice related to challenge design and reporting.</p> <p>[1] Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A., Feldmann, C., Frangi, A. F., Full, P. M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B. A., März, K., Maier, O., Maier-Hein, K., Menze, B. H., Müller, H., Neher, P. F., Niessen, W., Rajpoot, N., Sharp, G. C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Aziz Taha, A., van der Sommen, F., Wang, C.-W., Weber, M.-A., Zheng, G., Jannin, P., Kopp-Schneider, A.: Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions. arXiv preprint arXiv:1806.02051 (2018).</p>
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy: Summary statistics - Health records
<p>Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens including those from the herpes, retro- and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant. This variant has persisted throughout hominid evolution and could play a key role in regulation of the immune response. Using 121 infection and inflammation related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates haematopoietic pathways, and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation. </p> <p>-------------------------------------------------------------------------------------</p> <p>This dataset contains GWAS summary statistics for infection, inflammation, and allergy related traits in 487,297 individuals</p>
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy: Summary statistics - Serology
<p>Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens including those from the herpes, retro- and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant. This variant has persisted throughout hominid evolution and could play a key role in regulation of the immune response. Using 121 infection and inflammation related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates haematopoietic pathways, and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation. </p> <p>-------------------------------------------------------------------------------------</p> <p>This dataset contains GWAS summary statistics for quantitative antibody responses in 9611 individuals and results for a meta-analysis of UK Biobank and CoLaus/PsyCoLaus antibody responses.</p> <p> </p>
Data from Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies
<p>This dataset contains three text files in RIS format. They represent the screening process during study selection for "Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies" (<a href="https://doi.org/10.1111/liv.14179">https://doi.org/10.1111/liv.14179</a>). The file DOKU_All TiAb-Screening_20200116_cap contains all 3453 records (merged from original and update search) that were subjected to title-abstract screening. The file DOKU_All FT-Screening_20200116_cap contains all 174 records that were subjected to full-text screening. The file DOKU_All Included_20200116_cap contains all 26 records that were included into the final review.</p> <p>In addition, a PRISMA flow diagram (Fig. 1 in the paper) is available in TIF format.</p>
Dataset for "Intra-intestinal analysis of the functional activity of microbiomes and its application to the common marmoset intestine"
<p>Dataset:</p> <p>data1_ExpressionProfile.xlsx: gene expression profile</p> <p>data2_AnnotationProfile.xlsx: gene annotation profile</p>
Intra-intestinal analysis of the functional activity of microbiomes and its application to the common marmoset intestine
<p>Supplementary Table Captions:</p> <p>Table S7. Assignment of genes to unknown gene clusters</p> <p>Table S8. AUC used to determine parameters for covariation analysis by benchmarking</p> <p>Table S9. Linked unknown gene clusters</p> <p>Table S10. Information on common marmosets</p> <p>Table S11. Dual index sequences</p> <p>Table S12. Sequencing statistics</p> <p>Table S13. RIN score of total RNA samples</p> <p>Table S14. Top 20 bacterial species in taxonomic profiling</p> <p>Table S15. Number of genes covered by the nonchimaeric genome in each parameter at the assembly step</p> <p>Table S16. Number of genes covered by the nonchimaeric genome in each parameter at the merge step</p> <p>Table S17. Distance between intestinal sites</p>
Fig. 1 in Comparative Analysis Of Alien Red Oak (Quearcus Rubra L.) And Native Common Oak (Quercus Robur L.) Vegetation In Lithuania
Fig. 1. Detrented Correspondence Analysis of Quercus rubra () and Quercus robur of TilioCarpinetum stachyetosum () and Tilio-Carpinetum typicum types () plots
Jamendo content analysed with the Audio Commons Analysis Service
<p>This dataset contains partial output of running the final release of the <a href="https://github.com/AudioCommons/faas-ac-analysis">Audio Commons Analysis Service</a> on 99960 music pieces of the <a href="https://licensing.jamendo.com">Jamendo Licensing</a> catalogue. The service is described in <a href="https://www.audiocommons.org/assets/files/AC-WP4-QMUL-D4.13%20Release%20of%20tool%20for%20the%20automatic%20semantic%20description%20of%20music%20pieces.pdf">Deliverable D4.13</a> of the Audio Commons project. The analysis results are comprised of chord output including confidence for all pieces (using the software described in the deliverable) and of the output of the Essentia music extractor (v2.1_beta4-447-gc5ea2738) for 77190 of the pieces (of which tempo, beats, tuning and global-key are exposed through the API of the analysis service).</p> <p>The dataset is formatted as a single JSON file containing an array of documents. Each document in the array has two or three top-level keys. The first key is "_id" with values of the form "jamendo-tracks:<jamendo-id>". This id can be used to request metadata and audio through the <a href="https://developer.jamendo.com">Jamendo API</a>. The second key of the document is "chords", containing a subdocument with the output of the chord extraction algorithm. The third key is "essentia-music", which contains a subdocument with the output of the Essentia music extractor.</p> <div> </div>
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe. in Revision of common snipe, Gallinago gallinago in morphometric analysis and building the standard reference haematological values for further studies
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe.
Fig. 3 in Phylogenetic analysis of the Common Krait (Bungarus caeruleus) in Pakistan based on mitochondrial and nuclear protein coding genes
Fig. 3. Mitochondrial and nuclear genes (ND4, Cyt b, COI, 12S rRNA, 16S rRNA, C-mos, RAG-1, and NT3) Bayesian phylogeny for Common Krait (Bungarus caeruleus).
Fig. 2 in Phylogenetic analysis of the Common Krait (Bungarus caeruleus) in Pakistan based on mitochondrial and nuclear protein coding genes
Fig. 2. Mitochondrial and nuclear genes (ND4, Cyt. b, COI, 12S rRNA, 16S rRNA, C-mos, RAG-1, NT3, and BDNF) based Maximum Likelihood phylogeny for Common Krait (Bungarus caeruleus).
Figure 3 in Systematics, variation, and developmental instability: analysis of spine patterns in ancestrulae of a common bryozoan
Figure 3. Seasonal trends in frequencies of 3:3:1 (black) and variant (grey) spine patterns for the first-generation ancestrulae over the course of the 7-week sampling period from 14 July to 25 August. The ratio of variants to 3:3:1 and % variants are included.
Figure 1 in Systematics, variation, and developmental instability: analysis of spine patterns in ancestrulae of a common bryozoan
Figure 1. (A) SEM of an ancestrula of Bugula stolonifera: this ancestrula has a spine pattern of 4:3:1 (see Figure 2); (B) diagram of the position of left and right distal margins and position of the proximal spine.
Figure 2 in Systematics, variation, and developmental instability: analysis of spine patterns in ancestrulae of a common bryozoan
Figure 2. Some common spine pattern formulae and views of the frontal membrane of ancestralae of Bugula stolonifera. All spine patterns are recorded from the viewpoint of the ancestrula right:left:proximal. (A) Diagram of an ancestrula with the typical 3:3:1 spine pattern cited in the text; (B) 3:3:0 variant spine pattern resulting from a loss of the proximal spine: this spine pattern was the most abundant variant spine pattern accounting for 54% of the variant spine patterns; (C) 3:2:1 variant spine pattern resulting from a loss on the left distal margin; (D) 4:3:1 variant spine pattern resulting from a spine gain on the right distal margin.
Data from: Trophic plasticity in a common reef-building coral: Insights from δ13C analysis of essential amino acids
1. Reef-building corals are mixotrophic organisms that can obtain nutrition from endosymbiotic microalgae (autotrophy) and particle capture (heterotrophy). Heterotrophic nutrition is highly beneficial to many corals, particularly in times of stress. Yet the extent to which different coral species rely on heterotrophic nutrition remains largely unknown because it is challenging to quantify. 2. We developed a quantitative approach to investigate coral nutrition using carbon isotope (δ13C) analysis of six essential amino acids (AAESS) in a common Indo-Pacific coral (Pocillopora meandrina) from the fore reef habitat of Palmyra Atoll. We sampled particulate organic matter (POM) and zooplankton as the dominant heterotrophic food sources in addition to the coral host and endosymbionts. We also measured bulk tissue carbon (δ13C) and nitrogen (δ15N) isotope values of each sample type. 3. Patterns among δ13C values of individual AAESS provided complete separation between the autotrophic (endosymbionts) and heterotrophic nutritional sources. In contrast, bulk tissue δ13C and δ15N values were highly variable across the putative food sources and among the coral and endosymbiont fractions, preventing accurate estimates of coral nutrition on Palmyra. 4. We used linear discriminant analysis to quantify differences among patterns of AAESS δ13C values, or 'fingerprints', of the food resources available to corals. This allowed for the development of a quantitative continuum of coral nutrition that can identify the relative contribution of autotrophic and heterotopic nutrition to individual colonies. Our approach revealed exceptional variation in conspecific colonies at scales of meters to kilometers. On average, 41% of AAESS in P. meandrina on Palmyra are acquired via heterotrophy but some colonies appear capable of obtaining the majority of AAESS from one source or the other. 5. The use of AAESS δ13C fingerprinting analysis offers a significant improvement on the current methods for quantitatively assessing coral trophic ecology. We anticipate that this approach will facilitate studies of coral nutrition in the field, which are essential for comparing coral trophic ecology across taxa and multiple spatial scales. Such information will be critical for understanding the role of heterotrophic nutrition in coral resistance and/or resilience to ongoing environmental change.
Figure 2 in Comparative analysis of brain in relation to the body length and weight of common carp (Cyprinus carpio) in captive (hatchery) and wild (river system) populations
Figure 2. brain of common carps in wild environment.
Figure 1 in Comparative analysis of brain in relation to the body length and weight of common carp (Cyprinus carpio) in captive (hatchery) and wild (river system) populations
Figure 1. brain of common carps in captive environment.
Binary data file used for analysis_common_envelope unit testing in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is automatically downloaded as part of the Phantom github actions tests **</p> <p>This is an example snapshot from a Phantom simulation of a common envelope interaction, taken from the paper by <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.3181G">González-Bolívar et al. (2022)</a>. It is posted here primarily in order to perform unit and regression testing on the <a href="https://github.com/danieljprice/phantom/blob/master/src/utils/analysis_common_envelope.f90">analysis_common_envelope</a> module in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>).</p>
Fig. 1 in Phylogenetic analysis of the Common Krait (Bungarus caeruleus) in Pakistan based on mitochondrial and nuclear protein coding genes
Fig. 1. Sample collection sites in Pakistan for Common Krait (Bungarus caeruleus).
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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.