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342 results for “Electron Microscopy”
Electron microscopy of SARS-CoV particles - Dataset 06
<p>The dataset contains 111 transmission electron microscopy images of ultrathin (45 nm) plastic sections through extracellular SARS-CoV particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimensions at 0.54 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 06 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Scanning electron microscopy image dataset -- Abundances and morphotypes of the coccolithophore Emiliania huxleyi in southern Patagonian fjords and channels
<p>Data set 1: E.huxleyi_morphotypes_Patagonia.zip</p> <p>Scanning electron microscopy images of <em>Emiliania huxleyi</em> cells found inhabit the southern Patagonia fjords during the late-spring 2015 and early-spring 2017.</p> <p> </p> <p>Data set 2: E.huxleyi_abundances_Patagonia.zip</p> <p>Scanning electron microscope images of filters of plankton samples taken in 2015 and 2017 throughout southern Patagonia fjords.</p> <p>The "m" in sample name refer to the depth from which the sample was obtained. </p> <p>Tables are provided to associate <em>Emiliania huxleyi</em> morphotypes' counts and taxonomic identifications to environmental variables from the samples for which data was used in statistical analysis.</p>
Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy
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Atlantic Meridional Transect (AMT) 14: scanning electron microscopy images of the coccolithophore community
<p>Legacy scanning electron microscopy (SEM) images of plankton samples that were taken during Atlantic Meridional Transect cruise 14 (AMT 14), which sailed between the Faulkland Islands and the UK in 2004 (for more information see https://www.amt-uk.org/Cruises/AMT14 and the link to the cruise report therein). The SEM methodology applied to generate these images is described in Poulton et al. (2017) Coccolithophore ecology in the tropical and subtropical Atlantic Ocean: New perspectives from the Atlantic meridional transect (AMT) programme. <em>Prog. Oceanogr.</em> <strong>158</strong>, 150–170 and follows Charalampopoulou et al. (2011) Irradiance and pH affect coccolithophore community composition on a transect between the North Sea and the Arctic Ocean. <em>Mar. Ecol. Prog. Ser.</em> <strong>431</strong>, 25–43. </p> <p>The 16 samples currently in this entry are from nine CTD stations of AMT14. There is a unique, compressed (.zip) file of SEM images for each sample (CTD station and water depth) with the following naming scheme: </p> <p>AMT14_CTD#_A_WD,</p> <p>where CTD# denotes CTD number (e.g. CTD6), A denotes percentage of surface irradience level (e.g. 55 is 55% of surface irradience), and WD is water depth (m) of sample. </p> <p> </p> <p>Each compressed sample folder contains ca. 600-700 SEM images in TIF file format. The name of each image relates to the date (day_month_image number) the image was taken on. </p> <p> </p> <p>Correspondence should be directed to: Alex J. Poulton, Heriot-Watt University (a.poulton@hw.ac.uk)</p> <p>Please cite this compilation of SEM images in full (including doi) and acknowledge the Atlantic Meridional Transect programme if you are using these images: "AMT 14 was supported by the UK Natural Environment Research Council (NERC) through the Atlantic Meridional Transect consortium (NER/O/S/2001/00680)"</p> <p> </p> <p><strong>Associated publications:</strong><br>Poulton, A. J., Holligan, P. M., Charalampopoulou, A. & Adey, T. R. Coccolithophore ecology in the tropical and subtropical Atlantic Ocean: New perspectives from the Atlantic meridional transect (AMT) programme. <em>Prog. Oceanogr.</em> <strong>158</strong>, 150–170 (2017).</p> <p>Sheward, R. M., Poulton, A.J., Young, J.R., de Vries, J., Monteiro, F.M. & Herrle, J.O. Cellular morphological trait dataset for extant coccolithophores from the Atlantic Ocean. Submitted to <em>Scientific Data</em> in Janurary 2024.</p>
Figure 5 from: Zhao Y, Zhao F, Paton AJ, Xiao J-F, Chen Y-P, Xiang C-L (2024) Using scanning electron microscopy and molecular data to discover a new species from old herbarium collections: The case of Phlomoides henryi (Lamiaceae, Lamioideae). PhytoKeys 238: 127-146. https://doi.org/10.3897/phytokeys.238.117180
Figure 5 Phlomoides henryi Y.Zhao & C.L.Xiang A habitat B plant with linear-tuberous roots C inflorescence D verticillaster E flowers F dissected flower G appendages at base of posterior filaments H fruiting calyces I dissected calyces J bracts K floral leaves L stem leaves. Photographs by Yue Zhao, except C by Li Chen.
Figure 4 from: Zhao Y, Zhao F, Paton AJ, Xiao J-F, Chen Y-P, Xiang C-L (2024) Using scanning electron microscopy and molecular data to discover a new species from old herbarium collections: The case of Phlomoides henryi (Lamiaceae, Lamioideae). PhytoKeys 238: 127-146. https://doi.org/10.3897/phytokeys.238.117180
Figure 4 SEM of both sides of leaves of Phlomoides henryi and related species A, BP. henryiC, DP. bracteosaE, FP. brevifloraG, HP. macrophyllaI, JP. nyalamensisK, LP. tibeticaM, NP. milingensisO, PP. rotataA, C, E, G, I, K, M, OSEM of adaxial leaves B, D, F, H, J, L, N, PSEM of abaxial leaves.
Figure 2 from: Zhao Y, Zhao F, Paton AJ, Xiao J-F, Chen Y-P, Xiang C-L (2024) Using scanning electron microscopy and molecular data to discover a new species from old herbarium collections: The case of Phlomoides henryi (Lamiaceae, Lamioideae). PhytoKeys 238: 127-146. https://doi.org/10.3897/phytokeys.238.117180
Figure 2 Different types of trichomes of PhlomoidesA short simple non-glandular trichomes (P. macrophylla) B short simple non-glandular trichomes (P. breviflora) C long simple non-glandular trichomes (P. henryi) D symmetrically non-glandular stellate (P. breviflora) E non-glandular stellate with central long branch (P. bracteosa) F bi- or trifurcate non-glandular stellate (P. nyalamensis) G sub-sessile/ sessile glandular trichomes (P. macrophylla) H simple glandular trichomes of (P. bracteosa) I branched glandular trichomes (P. breviflora).
Figure 1 from: Zhao Y, Zhao F, Paton AJ, Xiao J-F, Chen Y-P, Xiang C-L (2024) Using scanning electron microscopy and molecular data to discover a new species from old herbarium collections: The case of Phlomoides henryi (Lamiaceae, Lamioideae). PhytoKeys 238: 127-146. https://doi.org/10.3897/phytokeys.238.117180
Figure 1 Phylogeny of Phlomoides inferred by Bayesian Inference (BI), based on the combined plastid dataset cpDNA. Support values displayed on the branches follow the order BI-PP/ML-BS (" * " indicates PP = 1.00 or BS = 100%, "-" indicates incongruent relationship between BI and ML tree.
Figure 3 from: Zhao Y, Zhao F, Paton AJ, Xiao J-F, Chen Y-P, Xiang C-L (2024) Using scanning electron microscopy and molecular data to discover a new species from old herbarium collections: The case of Phlomoides henryi (Lamiaceae, Lamioideae). PhytoKeys 238: 127-146. https://doi.org/10.3897/phytokeys.238.117180
Figure 3 Photos of bracts, SEM of bracts of Phlomoides henryi and related species A, BP. henryiC, DP. bracteosaE, FP. brevifloraG, HP. macrophyllaI, JP. nyalamensisK, LP. tibeticaM, NP. milingensisO, PP. rotata. A, C, E, G, I, K, M, O photos of bracts B, D, F, H, J, L, N, PSEM of bracts.
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 05
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 05 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR. The recorded area shows the profiles of a few ciliated cells which are structurally impaired by vesiculation, probably due to the sampling and storage before fixation. Only few organelles are detectable, including nucleus and some of the mitochondria. The cells are surrounded by cellular debris and heterogenous material (e.g. membrane lamella, needle-like crystals, round profiles with a fine-fibrous matrix). Virus particles are not visible.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Transmission electron microscopy (TEM) image datasets of peptide / protein nanowire morphologies
<p>TEM image dataset containing four nanowire morphologies of bio-derived protein nanowires and synthetic peptide nanowires.</p> <p>The peptide / protein nanowires used in this study were synthesized and imaged by Brian Montz in Prof. Todd Emrick's research group at the Department of Polymer Science and Engineering Department, University of Massachusetts Amherst. </p> <p>We acknowledge financial support from the U.S. National Science Foundation, Grant NSF DMREF #1921839 and DMREF #1921871.</p> <p>Nanowires were classified into either of the four morphologies: bundle, singular, dispersed or network. Each morphology contains 100 images (jpg files).</p> <p>For the dispersed and network morphologies, because these two morphologies are harder to visually distinguish, we have created manual segmentation labels of the nanowires (included in these two morphology folders as png files). Percolation analysis was done on these manually segmented nanowires to provide quantitative metric on whether the nanowires form a network in the image. </p> <p>seg_mask_5_resolutions.zip contains ground truth 2D binary encoding of segmented nanowires at 5 resolutions.</p> <p>encoders_trained_with_optimized_hyperparameter.zip contains 4 sets of encoders trained with either SimCLR or Barlow-Twins self-supervised methods on either generic TEM images, or generic everyday photographic images (each with 5 replicates with different random seed) with optimized hyperparameters.</p> <p>Open-access datasets that have been used during self-supervised training.</p> <ul> <li>2021-CEM500K.zip contains 10,000 images that was used as "generic TEM images" to train the encoders with self-supervised methods, these are a random selection from the CEM500k open-access dataset. DOI: <a href="https://doi.org/10.7554/eLife.65894">10.7554/eLife.65894</a></li> <li>2022-1000-ImageNet.zip contains 1,000 images from the ImageNet1k dataset, each come from a different category. DOI: <a href="http://doi.org/10.1007/s11263-015-0816-y">10.1007/s11263-015-0816-y</a></li> </ul> <p>Open-access datasets that our machine learning workflow have been applied to:</p> <ul> <li>2022-AutoDetect-mNP-morphology.zip contains a selected TEM images of nanoparticles categorized in 3 morphologies from the AutoDetect-mNP datasets: DOI: <a href="http://doi.org/10.6078/D1WT44">10.6078/D1WT44</a> and DOI: <a href="http://doi.org/10.6078/D1S12H">10.6078/D1S12H</a></li> <li>2021-TEM virus.zip contains TEM images of 9 types of viruses from the TEM virus dataset. Matuszewski, Damian; Sintorn, Ida-Maria (2021), “TEM virus dataset”, Mendeley Data, V3, DOI: <a href="http://doi.org/10.17632/x4dwwfwtw3.3">10.17632/x4dwwfwtw3.3</a></li> </ul> <p>The official github page of the implementation of the machine learning models is <a href="https://github.com/arthijayaraman-lab/semi-supervised_learning_microscopy_images">semi-supervised_learning_microscopy_images</a>.</p> <p>If you use the dataset or the codes in the repository linked above, please cite the following <a href="https://doi.org/10.1039/D2DD00066K">manuscript</a>:</p> <p>S. Lu, B. Montz, T. Emrick and A. Jayaraman, <em>Digital Discovery</em>, 2022, <strong>1</strong>, 816-833 , <strong>DOI: </strong>10.1039/D2DD00066K</p>
German NFDI, FAIRmat-NFDI, NOMAD, NOMAD Oasis, pynxtools, pynxtools-em, NeXus, example datasets for electron microscopy
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Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy
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welborn_24_streaming_large_scale_electron_microscopy_data_si_videos
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◂Fig. 3 Historically described phenotypical variations and yet undiscovered deviations in the plate pattern of P. volzii. b–c, f, i, l–m Light microscopy, a, d–e, g–h, k scanning electron microscopy. a–f Newly identified deviations a–b plate 4′′ pentagonal in strains a GeoM*793; b GeoM*788; c plate 2a split (strain GeoK*024); d plates 2′′ and 3′′ fused (strain GeoM*866); e plates 1′′′ and 1′′′′ fused (strain GeoM*788); f plates 1a and 3′ fused (strain GeoM*788). g–m Historic infraspecific taxa; g P. guestrowiense forma lineatum (strain GeoM*866); h P. guestrowiense forma compressum (strain GeoM*866); i P. guestrowiense subvar. originale (strain GeoK*024); k P. volzii var. cinctiforme (strain GeoM*793); l P. volzii var. simplex (strain GeoM*789); m P. volzii forma complexum (strain GeoM*793). Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, split or fused plates are indicated by asterisks. Scale bar= 10 µm. U A= 15 kV in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 3 Historically described phenotypical variations and yet undiscovered deviations in the plate pattern of P. volzii. b–c, f, i, l–m Light microscopy, a, d–e, g–h, k scanning electron microscopy. a–f Newly identified deviations a–b plate 4′′ pentagonal in strains a GeoM*793; b GeoM*788; c plate 2a split (strain GeoK*024); d plates 2′′ and 3′′ fused (strain GeoM*866); e plates 1′′′ and 1′′′′ fused (strain GeoM*788); f plates 1a and 3′ fused (strain GeoM*788). g–m Historic infraspecific taxa; g P. guestrowiense forma lineatum (strain GeoM*866); h P. guestrowiense forma compressum (strain GeoM*866); i P. guestrowiense subvar. originale (strain GeoK*024); k P. volzii var. cinctiforme (strain GeoM*793); l P. volzii var. simplex (strain GeoM*789); m P. volzii forma complexum (strain GeoM*793). Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, split or fused plates are indicated by asterisks. Scale bar= 10 µm. U A= 15 kV
A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM): Datasets
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FIGURE 4 in A new blind snake of the genus Letheobia (Serpentes: Typhlopidae) from Rwanda with redescriptions of L. gracilis (Sternfeld, 1910) and L. graueri (Sternfeld, 1912) and the introduction of a non-invasive preparation procedure for scanning electron microscopy in zoology
FIGURE 4. Holotype of Letheobia akagerae sp. nov. (ZFMK 100862) in life.
Scanning electron microscopy datasets -- Emiliania huxleyi strains from naturally high and low CO2 waters responding to high and low CO2 in the lab
<p>Study question: How do Emiliania huxleyi strains isolated from naturally high CO2 waters or low CO2 waters respond to exposure to high and low CO2 levels?</p> <p> </p> <p>Associated article:<br> Peter von Dassow, Francisco Díaz-Rosas, El Mahdi Bendif, Juan-Diego Gaitán-Espitia, Daniella Mella-Flores, Sebastian Rokitta, Uwe John, and Rodrigo Torres. 2018. Over-calcified forms of the coccolithophore <em>Emiliania huxleyi </em>in high-CO2 waters are not preadapted to ocean acidification. Biogeosciences. <a href="https://doi.org/10.5194/bg-15-1-2018">https://doi.org/10.5194/bg-15-1-2018</a></p> <p> </p> <p>Technical notes:</p> <p>Three electron microscopes were used:</p> <ol> <li>TM3000 (Hitachi High-Technologies, Tokyo, Japan) in the Unidad de Microscopía Avanzada of the Facultad de Ciencias Biológicas, Pontificia Univesidad Católica de Chile. The Hitachi microscope is not of high quality, and, when available, other electron microscopes were used.</li> <li>Quanta 250 (FEI, Hillsboro, Oregon, USA) in the Facultad de Geología, Universidad de Chile</li> <li>Quanta FEG 250 (FEI, Hillsboro, Oregon, USA) in the laboratory CIEN-UC, Facultad de Física, Pontificia Universidad Católica de Chile.</li> </ol> <p> </p> <p>Data set 1: Data-sharing-SEM_Calfuco-CO2 experiment.zip</p> <p>Scanning electron microscopy images of E. huxleyi strains after bubbling with 1200 µatm CO2 and 400 µatm CO2 air/CO2 mixes.</p> <p> </p> <p>Data set 2: Field-SEM-2011-2013.zip</p> <p>Scanning electron microscope images of filters of plankton samples taken during field campaigns. See article for methodology. For the samples from ElQuisco_2012 and JuanFernandez_2011, note that the last two digits in the sample name refer to the depth from which the sample was obtained (ej., “FQ.01.01.05D” is from 5 m and “FQ.01.01.15D” is from 15 m). Tables are provided to associate counts and taxonomic identifications to environmental variables from the samples for which data was used in statistical analysis. Note also that images do not correspond to all counts reported, as sometimes counts were made without capturing images due to time pressure for microscope use. </p> <p> </p>
FIGURE 5 in Description of the egg of Hulecoeteomyia koreica (Edwards) (Diptera: Culicidae) using scanning electron microscopy
FIGURE 5. Micropyle and anterior part of egg of Hulecoeteomyia koreica. Scale bar = 20 µm.
FIGURE 5 in Description of the eggs of Psorophora ciliata and Psorophora ferox (Diptera: Culicidae, Aedini) from the east of the Brazilian state of Santa Catarina using scanning electron microscopy
FIGURE 5. Tubercles of the outer chorion of the egg of Psorophora ferox (A, 650x; B, 500x).
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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.