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Fig. 3 in Applications and limitations of micro-XCT imaging in the studies of Permian radiolarians: A new genus with bi-polar main spines
Fig. 3. Schematic drawings on internal structure of Permian and Mesozoic spumellarians with bi-polar main spines (detailed information including source is summarized in Table 2). A, B. Protopsium. C. Pegoxystris. D. Falcispongus. E. Spongoxystris. F. Dalongicaepa Xiao and Suzuki gen. nov. G. Spongotortilispinus. H. Spongopallium. I, J. Archaeospongoprunum. K. Palaeospongurus. L. Paroertlispongus.
Fig. 2 in Applications and limitations of micro-XCT imaging in the studies of Permian radiolarians: A new genus with bi-polar main spines
Fig. 2. Micro-XCT cross-sectional images (A, B), SEM micrographs (C–E), and TLM micrographs (F) of the spumellarian Tetraspongodiscus stauracanthus Feng in Feng et al., 2006 from Changhsingian of the Rencunping section (A–D) and Dongpan (E), South China and upper Capitanian of West Texas, USA (F). A. LGFEC SR 33-201. B. LGFEC SR 33-202. C. LGFEC SR 36-011. D. LGFEC SR 33-022. E. DP 4/4860 (from Feng et al. 2006; copyright is permitted from Elsevier). F. S3-2A (from Noble and Jin 2010). Different cross sections show clear hoops with few primary beams and a possible median bar (A1 and B1), the centrical internal spicule (A2), and the embedded proximal part of the main spines (B2). Scale bars 50 μm.
FIGURE 11 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 11. Fossils (bones and teeth) and a mixture of fossils and sediment (sand and gravel) from Sample 3. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image (grayscale) under UV light. 4, Black and white image after segmentation of the blue channel image.
FIGURE 9. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 9. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3.
FIGURE 10 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 10. Examples of spectral images of Sample 3 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.
FIGURE 4 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 4. Examples of spectral images of Sample 1 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB image computed using the sRGB - standard RGB colour space is also provided.
FIGURE 8 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 8. RGB images of Sample 2 with ferrous particles, bones-teeth, sand-gravel and a mixture of bonesteeth and sand-gravel. 1, Under daylight (D65). 2, Under UV Light.
FIGURE 3. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 3. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1. 2, Fluorescence spectra (radiance in W/sr·cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1.
FIGURE 6. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 6. 1, Reflectance spectra of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2.
FIGURE 1. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 1. 1, Wet sieving process of palaeontological samples using Freudenthal's technique. 2, Visual recognition and separation of teeth and bones using a binocular microscope and pincers.
FIGURE 7 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 7. Examples of spectral images of Sample 2 through spectral bands (420, 520, 620, 670 and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.
FIGURE 5 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 5. Mixture of bones-teeth and sand-gravel from Sample 1. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image under UV light. 4, Green channel image (grayscale) under UV light. 5, Black and white image after segmentation of the green channel image.
CT Images from the APOLLO-5-LSCC study for Body Part Regression Tutorial
<p>The dataset includes CT images from the <a href="https://wiki.cancerimagingarchive.net/display/Public/APOLLO-5-LSCC#95224279953c510266704797bf4c0bb2e8e7e04f">APOLLO-5-LSCC</a> study for a tutorial from the <a href="https://github.com/MIC-DKFZ/BodyPartRegression">Body Part Regression</a> python package. The CT images are saved in the npy-format. Moreover, an additional Excel file exists, which saves for each image the corresponding pixel spacings in x, y and z-direction.</p> <p>The original data was generated by the Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Research Network, a Federal Precision Oncology and Cancer Moonshot Program of the Department of Defense, Department of Veterans Affairs, and National Cancer Institute.</p>
Images, graphs and tables from the article: Biological performance of a bioabsorbable Poly (L-Lactic Acid) produced in polymerization unit: in vivo studies -
<p>The images, graphs and tables attached correspond to the study performed in the thesis project on the in vivo biocompatibility of the PLLA polymer produced.</p>
Biological performance of a bioabsorbable Poly (L-Lactic Acid) produced in polymerization unit: in vivo studies - HEMATOXYLIN&EOSIN, MASSON AND CT SCAN IMAGES
<p>CT, hematoxylin & eosin and masson staining images of the animals in the experimental biocompatibility study.</p> <p>Groups at 6 months post implantation and groups at 9 months post implantation.</p> <p>In addition to the subgroups lesion with PLLA and lesion without PLLA</p>
Full maize tassel image set in the study of Tasselyzer version 1
<p>Male fertility in maize is controlled by development and genetic programming and is directly impacted by environmental factors such as light, temperature, water, and nutrient availability; the control of this trait has substantial agronomic utility. Maize anthers emerge from male florets, which are clustered to form the tassel at the top of the plant separated from the female ear. Quantification of anther extrusion is one important aspect in the determination of male fertility. To address the lack of an automated method to measure anther extrusion on a large scale, we developed “Tasselyzer'', a quantitative, image-based color trait analysis pipeline for tassel image segmentation, based on the existing PlantCV platform, and we applied it to determine the proportion of anther extrusion. We evaluated Tasselyzer in maize during the seven-day period of pollen shedding as well as in the temperature-sensitive male sterile mutant <em>dcl5</em>. With tassel images obtained with a smart phone camera, we show that the anther scores positively correlate with anther extrusion, and such methods can be used to measure environmental impacts on the <em>dcl5</em> mutant. Altogether, this work establishes an automated and inexpensive method to quantify anther extrusion in maize, which would be useful for research and breeding. </p> <p>Here is the full image set was used in Tasselyzer version 1. </p>
Trapalyzer: A computer program for quantitative analyses in fluorescent live-imaging studies of Neutrophil Extracellular Trap formation.
<p>This data set contains a set of fluorescent microscopy images of a co-culture of neutrophil cells and E. coli bacteria used to study the Neutrophil Extracellular Trap (NET) formation stimulated by bacteria. </p> <p>NETs and live cells were visualized with a double fluorescent staining of DNA using Hoechst 33342 and SYTOX Green. </p> <p><strong>Reagents.</strong></p> <p>Roswell Park Memorial Institute (RPMI) 1640 medium, HEPES, SYTOX<sup>TM</sup> Green, and Hoechst 33342 were purchased from Thermo Fisher Scientific (Waltham, USA). LB broth was purchased from Sigma Aldrich (St Louis, MO, USA).</p> <p><strong>Preparation of blood neutrophils.</strong></p> <p>Neutrophils were obtained from peripheral blood of one healthy blood donor. Blood sample was purchased at Local Blood Donation Centre and according to local regulations, the blood donor enabled blood donation center to sell their blood samples for scientific purposes and the consent of bioethical committee was not required. Blood was collected into a citrate tube and processed within 2 hours from collection. Neutrophils were isolated using density gradient centrifugation followed by polyvinyl alcohol sedimentation, exactly as described in [1]. Isolated neutrophils were suspended in RPMI 1640 medium with 10 mM HEPES (RH). </p> <p><strong>Preparation of bacteria.</strong></p> <p><em>Escherichia coli</em> (American Type Culture Collection(ATCC) 25922 strain) were grown overnight in LB broth with shaking. In the morning, an aliquot of bacterial culture was taken, diluted 100 x in a fresh LB medium and grown for subsequent 2-3 hours. Subsequently, bacterial cultures were washed and resuspended in RH medium.</p> <p><strong>Co-culture of neutrophils with bacteria</strong><br> Neutrophils were seeded into the wells of 48-well plates at the density of 2 ⨉ 10<sup>4</sup> cells/well and allowed to settle for 30 minutes at 37°C, 5% CO2. Subsequently, <em>E. coli</em> was added into the appropriate wells at the multiplicity of infection of 4 or 1 (<em>E.coli</em>: neutrophil). Neutrophils incubated without bacteria were used as a control group. A technical duplicate for each condition was prepared. <br> For each intended timepoint (t=0, 60, 90, 120, 180 minutes), a separate 48 well plate was prepared. The plates were centrifuged for 5 minutes at 250 g to allow the contact of bacteria with neutrophils. The plates were incubated at 37°C, 5\% CO2 for a specified time and then the samples were stained with SYTOX<sup>TM</sup> Green (100 nM) and Hoechst 33342 (1.25 μM) for 10 minutes. Four images of each well were taken with Leica DMi8 fluorescent microscope equipped with a 10× magnification objective (Leica, Wetzlar, Germany). Overall, 120 images have been obtained.</p> <p> </p> <p><strong>2019_04_24--ecoli_neu_tiff_channel_merged.zip:</strong> Images in .tif format, each containing 5 channels: channel 1 for SYTOX Green fluorescent stain (green fluorescence), channel 2 for Hoechst 33342 fluorescent stain (blue fluorescence), and three channels for transmission light encoded in RGB values. </p> <p> </p> <p><strong>2019_04_24--ecoli_neu_tiff_raw_exported.zip:</strong> Images split by different light sources: transmission light (_ch00.tif), SYTOX Green fluorescence (_ch01.tif), Hoechst 33342 fluorescence (_ch02.tif).</p> <p> </p> <p>[1] Bystrzycka W, Moskalik A, Sieczkowska S, Manda-Handzlik A, Demkow U, Ciepiela O. The effect of clindamycin and amoxicillin on neutrophil extracellular trap (NET) release. <em>Cent Eur J Immunol</em>. 2016;41(1):1-5. doi:10.5114/ceji.2016.58811</p>
Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].
Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.
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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.