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1,876 results for “pathology”
Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets
<p>The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms – AdaBoost and Random Forests – on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.</p>
Dental, pathological, and UHPLC data from Middenbeemster archaeological site
<p>Datasets used in 'Multiproxy analysis exploring patterns of diet and disease in dental calculus and skeletal remains from a 19th century Dutch population' (<a href="https://doi.org/10.24072/pcjournal.414">https://doi.org/10.24072/pcjournal.414</a>).</p> <p><strong>Changes</strong></p> <p>v1.0.1: added <em>data-dictionary.md</em> file</p> <p>Newest v1.0.0: Upload the correct <em>LICENSE</em> file</p>
Supplemented material to "Mycobacteriosis in various pet and wild birds from Germany: pathological findings, coinfections, and characterisation of causative Mycobacteria."
<p>This is the supplemented material to the publication "Mycobacteriosis in Various Pet and Wild Birds from Germany: Pathological Findings, Coinfections, and Characterization of Causative Mycobacteria". <br>The causative agents and confounding factors of mycobacteriosis in a set of pet (n=45) and some wild birds (n=5) from Germany were examined in this study. Not only Mycobacterium genavense (Mg), but also M. avium subsp. avium (Maa) and M. avium subsp. hominissuis (Mah), contributed to mycobacteriosis in these birds. The isolates were characterized by a combination of different typing methods. The genetic diversity of isolates belonging to Mg, Maa and Mah differed. Various coinfections by viruses, endoparasites, fungi and other bacterial species did not affect the manifestation of mycobacteriosis. Cross pathological fidings were more often seen in mycobacteriosis caused by Ma compared to Mg suggesting a different pathogenicity of the two species. New genotypes of Mah were identified in these birds that is important for epidemiological studies and for understanding the zoonotic role of this pathogen, as the subsp. hominissuis represents an increasing public health concern. The study provides some evidence of correlation between individual Maa genotypes and virulence which will have to be confirmed by broader studies.</p>
Testing whole slide image for OpenPhi - Open Pathology Interface
<p>An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.</p>
Artefact segmentation in digital pathology whole-slide images
<p>Dataset with examples of Artefacts in Digital Pathology.</p> <p>The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.</p> <p>The dataset is split in different folders:</p> <ul> <li>train <ul> <li>18 whole-slide images (extracted at 1.25x & 2.5x magnification)</li> <li>All from the same Block (colorectal cancer tissue)</li> <li>1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.</li> </ul> </li> <li>validation <ul> <li>3 whole-slide images (1.25x + 2.5x mag)</li> <li>2 from the same Block as the training set (1 IHC, 1 H&E)</li> <li>1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)</li> </ul> </li> <li>validation_tiles <ul> <li>patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.</li> <li>7 patches from each slide.</li> </ul> </li> <li>test <ul> <li>1 whole-slide image (1.25x + 2.5x mag)</li> <li>From another block: IHC staining (anti-NR2F2), mouth cancer</li> </ul> </li> </ul> <p>For the train, validation and test whole-slide images, each slide has:<br> - The RGB images @1.25x & 2.5x mag<br> - The corresponding background/tissue masks<br> - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)</p> <p>For the validation tiles, the following table gives the "patch-level" supervision:</p> <p>tile# Artefact(s)<br> 00 None/Few<br> 01 Tear&Fold<br> 02 Ink<br> 03 None/Few<br> 04 None/Few<br> 05 Tear&Fold<br> 06 Tear&Fold + Blur<br> 07 Knife damage<br> 08 Knife damage<br> 09 Ink<br> 10 None/Few<br> 11 Tear&Fold<br> 12 Tear&Fold<br> 13 None/Few<br> 14 None/Few<br> 15 Knife damage<br> 16 Tear&Fold<br> 17 None/Few<br> 18 None/Few<br> 19 Blur<br> 20 Knife damage</p>
Data from: Chronic Rapamycin administration via drinking water mitigates the pathological phenotype in a Krabbe disease mouse model through autophagy activation.
<p>ABSTRACT </p><p>Krabbe disease (KD) is a rare disorder caused by a deficiency of the lysosomal enzyme galactosylceramidase (GALC), resulting in the accumulation of the cytotoxic metabolite psychosine (PSY) in the nervous system. This accumulation triggers demyelination and neurodegeneration. Despite ongoing research, the underlying pathogenic mechanisms remain incompletely understood, and there is currently no cure available.</p><p>Previous studies from our lab revealed the presence of autophagy dysfunctions in KD pathogenesis, as evidenced by the presence of p62-tagged protein aggregates in the brains of KD mice and increased p62 levels in the KD sciatic nerve. We also demonstrated that the autophagy inducer Rapamycin (RAPA) can partially restore the wild-type (WT) phenotype in KD primary cells by reducing the number of p62 aggregates.</p><p>In this study, we tested RAPA in the Twitcher (TWI) mouse, a spontaneous KD mouse model. We administered the drug ad libitum via drinking water (15 mg/L) starting from post-natal day (PND) 21-23. We longitudinally monitored the motor performance of the mice through grip strength and rotarod tests, along with various biochemical parameters related to KD pathogenesis (i.e. autophagy markers expression, myelination, astrogliosis, and PSY accumulation).</p><p>Our findings demonstrate that RAPA significantly enhances motor functions at specific treatment time points and reduces astrogliosis in TWI brain, spinal cord, and sciatic nerves. Using western blot and immunohistochemistry, we observed a decrease in p62 aggregates in TWI nervous tissues, which corroborates our earlier in-vitro results. Furthermore, RAPA treatment partially reduces PSY levels in the spinal cord.</p><p>In conclusion, our results support the consideration of RAPA as a supportive therapy for KD. Importantly, as RAPA is already available in pharmaceutical formulations for clinical use, its potential for KD treatment can be promptly evaluated in clinical trials.</p>
Cyberhate that targets people who are plus-size in the news: The role of bystanders in mitigating social pathologies (CYBERPLUS)
<p>The dataset was created for the project "Cyberhate that targets people who are plus-size in the news: The role of bystanders in mitigating social pathologies (CYBERPLUS)". The data was collected between July 12 and July 26, 2024, from 1,030 young Czech people aged 16-25. The survey asked young people about their sociodemographic information, attitudes toward and perceptions of entitativity of three groups (overweight people, underweight people, people with physical disabilities), group identification, bystander appraisals and behavioural intentions, hate speech perception, and internet use. It included an experimental part in which the participants were exposed as bystanders to social media news posts about overweight people and comments under the posts. The dataset is accompanied by a data dictionary and a technical report.</p>
Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction
<p>This dataset includes all the data and scripts needed to reproduce the analysis and results on the manuscript "Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction" (<a href="https://www.biorxiv.org/content/early/2018/04/22/306068">link</a>). The brain data is not raw, as T1w were not defaced. We will do so in the near future for version 2.0. Instead we include only the "output/thickness" files used in the final analysis. For the use of raw T1w images, please contact the main author EAGV.</p> <p> </p> <p>Note: Paths will differ in the script.</p>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Data For Scalco et al. Clinicopathological correlates of quantitative Amyloid-B Pathology in the Temporal Cortex: Machine learning analysis of 131 cases from an ADRC
<p>Dataset containing 131 de-identified whole slide images (WSIs) with a respective data dictionary. </p> <p><strong>Paper</strong>: Scalco, R., Oliveira, L.C., Lai, Z. et al. Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases. acta neuropathol commun 12, 134 (2024). https://doi.org/10.1186/s40478-024-01827-7</p> <p><strong>Details</strong>: A total of 131 .svs. WSIs, de-identified using svs-deidentifier v 0.9.1-beta (https://github.com/pearcetm/svs-deidentifier/releases). Dataset is uploaded in batches due to Zenodo data upload limitations.</p> <p><strong>Slide curation/preparation</strong>: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 between 20x and 40x magnification.</p> <p><strong>Code:</strong> Please refer to <a href="https://github.com/ucdrubinet/BrainSec">https://github.com/ucdrubinet/BrainSec</a> and <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p>
The spatial landscape of lung pathology during COVID-19 progression - raw IMC data
<p>Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.</p>
A statistical shape model of craniosynostosis patients and 100 model instances of each pathology
<p>This dataset is part of the publication "A statistical shape model for radiation-free assessment and classification of craniosynostosis" (M. Schaufelberger et al.). It includes several 3D head models constructed of surface scans of craniosynostosis patients: The full shape model, a texture model, and submodels of four classes: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly). Each of the models is available in an .h5 file. We also include 100 mesh instances as a .ply file in a zip file. The model's statistical information can be incorporated into the [Liverpool-York child head model (Dai et al. 2019)](https://doi.org/10.1007/s11263-019-01260-7) as it uses the same vertex order and IDs (starting from index 0). If you want to synthesize new models, take a look a the demo.py file. For information about the hierarchy in the h5-file, take a look at documentation.md.</p>
Portimine A toxin causes skin pathology through ZAKα-dependent NLRP1 inflammasome activation: LC-MS/MS raw data for Figure 1. C
<p>This dataset pertains to the LC-MS/MS analyses conducted as part of a study on microalgal toxins present in samples from Senegal, published in the paper entitled <em>"Portimine A toxin causes skin pathology through ZAK</em><em>α</em><em>-dependent NLRP1 inflammasome activation."</em> The data correspond to the quantification results of environmental samples presented in Figure 1C.</p> <p>The raw data were acquired using Analyst software (Applied Biosystems proprietary software). The materials and methods used to generate these data are detailed in the associated publication in <em>EMBO Molecular Medicine</em> (ISSN: 1757-4676, 2024).</p>
Evolutionary gain and loss of a pathological immune response to parasitism
<p><span>Parasites impose fitness costs on their hosts. Biologists often assume that natural selection favors infection-resistant hosts. Yet, when the immune response itself is costly, theory suggests selection may instead favor loss of resistance. Intraspecific variation in immune costs are rarely surveyed in a manner that tests evolutionary patterns, and there are few examples of adaptive loss of resistance. Here, we show that when marine threespine stickleback colonized freshwater lakes they gained resistance to the freshwater-associated tapeworm, <em>Schistocephalus solidus</em>. Extensive peritoneal fibrosis and inflammation is a commonly observed phenotype that contributes to suppression of cestode growth and viability, but also impose a substantial cost of reduced fecundity. Combining genetic mapping and population genomics, we find that opposing selection generates immune system differences between tolerant and resistant populations, consistent with divergent optimization.</span></p>
A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer
<p>Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer</p>
Digital Pathology Dataset for Prostate Cancer Diagnosis
<p>Links to code and <em>Patterns</em> paper:</p> <p>1. <a href="https://10.5281/zenodo.7152962">Multi-lens Neural Machine (MLNM) Code</a></p> <p>2. <a href="https://www.cell.com/patterns/fulltext/S2666-3899(22)00274-4">An AI-assisted Tool For Efficient Prostate Cancer Diagnosis in Low-grade and Low-volume Cases</a></p> <p>Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified.</p> <p>Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (<a href="https://computationalpathologygroup.github.io/ASAP/">ASAP</a>). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated.</p> <p> </p> <p><strong>Whole Slide Image Dataset </strong></p> <p>Whole Slide Image dataset containing 99 images in SVS format with corresponding annotations in XML format are provided in WSI.zip. Available patient grading for the WSIs are provided in 'gleason_score_mapped.txt'. These XML annotations can be parsed using the code in official repository.</p> <p><strong>Cropped Image Dataset </strong></p> <p>Patches of size 512 × 512 pixels were cropped from the WSI (Whole Slide Image Dataset) at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images.</p> <p>This dataset is used to train the two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows:</p> <ol> <li>gland_segmentation_dataset.zip</li> <li>gland_classification_dataset.zip</li> </ol> <p><strong>Table 1:</strong> The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen.</p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>#Slides</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>Train</p> </td> <td> <p>Valid</p> </td> <td> <p>Test</p> </td> <td> <p><strong>Total</strong></p> </td> </tr> <tr> <td> <p>Prostatectomy</p> </td> <td> <p>17</p> </td> <td> <p>8</p> </td> <td> <p>15</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>Biopsy</p> </td> <td> <p>26</p> </td> <td> <p>13</p> </td> <td> <p>20</p> </td> <td> <p>59</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>43</p> </td> <td> <p>21</p> </td> <td> <p>35</p> </td> <td> <p>99</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p><strong>#Patches</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>Train</p> </td> <td> <p>Valid</p> </td> <td> <p>Test</p> </td> <td> <p><strong>Total</strong></p> </td> </tr> <tr> <td> <p>Prostatectomy</p> </td> <td> <p>7795</p> </td> <td> <p>3753</p> </td> <td> <p>7224</p> </td> <td> <p>18772</p> </td> </tr> <tr> <td> <p>Biopsy</p> </td> <td> <p>5559</p> </td> <td> <p>4028</p> </td> <td> <p>5981</p> </td> <td> <p>15568</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>13354</p> </td> <td> <p>7781</p> </td> <td> <p>13205</p> </td> <td> <p>34340</p> </td> </tr> </tbody> </table> <p><strong>Table 2:</strong> The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. <strong>GS</strong>: Gleason Score. <strong>B</strong>: Benign. <strong>M</strong>: Malignant.</p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>#Slides (GS 3+3:3+4:4+3)</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>Train</p> </td> <td> <p>Valid</p> </td> <td> <p>Test</p> </td> <td> <p><strong>Total</strong></p> </td> </tr> <tr> <td> <p>Biopsy</p> </td> <td> <p>10:9:1</p> </td> <td> <p>3:7:0</p> </td> <td> <p>6:10:0</p> </td> <td> <p>19:26:1</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p><strong>#Patches (B:M)</strong></p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>Train</p> </td> <td> <p>Valid</p> </td> <td> <p>Test</p> </td> <td> <p><strong>Total</strong></p> </td> </tr> <tr> <td> <p>Biopsy</p> </td> <td> <p>1557:2277</p> </td> <td> <p>1216:1341</p> </td> <td> <p>1543:2718</p> </td> <td> <p>4316:6336</p> </td> </tr> </tbody> </table> <p><strong>NB:</strong> Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study.</p>
FIGURE 7 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 7. Location and geologic position of the Woodbine Group. A. General stratigraphic sequence and timescale for the Cretaceous of central and north central Texas showing the position of the Woodbine Group. Position of the AAS within the Woodbine is marked with an arrowhead. Terrestrial deposits represented by stippled intervals. Time scale based on Denne et al. (2016). Modified from Adams et al. (2011). B. Generalized map of geological units present as surface exposures in the Fort Worth basin with location of AAS shown. Modified after Strganac (2015) and Barnes et al. (1972).
FIGURE 4 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 4. Modern Trachemys scripta plastron elements (UTK 2317) with shell disease. Photograph (A) and orthographic model based on µCT data (B) shown in ventral view. Frames on the photograph and model highlight specific areas of shell disease, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Patches of shell disease are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
FIGURE 2 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 2. Fossil turtle shell fragments (DMNH 2013-07-1319) with putative bite marks. Photographs (A, G) and orthographic models based on µCT data (B, H) shown in external view. Frames on the photograph and model highlight specific areas with bite marks as both direct µCT data (C, E, I) and heatmapped slices illustrating bone density changes (D, F, J). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A, B, G, and H equal 2 cm. Scale bars in C, E, and I equal 5 mm.
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.