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1,944 results for “Class I”
FIGURE 4. Rhopalaea idoneta n in A new species of the genus Rhopalaea (Class: Ascidiacea) from the Red Sea
FIGURE 4. Rhopalaea idoneta n. sp. A. Dorsal tubercle B. Neural ganglion C. Dorsal lamina D. Stigmata shape. Scale bar 1mm. Photo: A. Gur
FIGURE 5. Rhopalaea idoneta n in A new species of the genus Rhopalaea (Class: Ascidiacea) from the Red Sea
FIGURE 5. Rhopalaea idoneta n. sp. rectal region. Arrows pointing out to the genital papilla and anus.
Who develops pandemic fatigue? Insights from latent class analysis
<p>According to the World Health Organization, pandemic fatigue poses a serious threat for managing COVID-19. Pandemic fatigue is characterized by progressive decline in adherence to social distancing (SDIS) guidelines, and is thought to be associated with pandemic-related emotional burnout. Little is known about the nature of pandemic fatigue; for example, it is unclear who is most likely to develop pandemic fatigue. We sought to evaluate this issue based on data from 5,812 American and Canadian adults recruited during the second year of the COVID-19 pandemic. Past-year decline in adherence to SDIS had a categorical latent structure according to Latent Class Analysis, consisting of a group adherent to SDIS (Class 1: 92% of the sample) and a group reporting a progressive decline in adherence to SDIS (i.e., pandemic fatigue; Class 2: 8% of the sample). Class 2, compared to Class 1, was associated with greater pandemic-related burnout, pessimism, and apathy about the COVID-19 pandemic. They also tended to be younger, perceived themselves to be more affluent, tended to have greater levels of narcissism, entitlement, and gregariousness, and were more likely to report having been previously infected with SARSCOV2, which they regarded as an exaggerated threat. People in Class 2 also self-reported higher levels of pandemic-related stress, anxiety, and depression, and described making active efforts at coping with SDIS restrictions, which they perceived as unnecessary and stressful. People in Class 1 generally reported that they engaged in SDIS for the benefit of themselves and their community, although 35% of this class also feared they would be publicly shamed if they did not comply with SDIS guidelines. The findings suggest that pandemic fatigue affects a substantial minority of people and even many SDIS-adherent people experience emotionally adverse effects (i.e., fear of being shamed). Implications for the future of SDIS are discussed.</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</p> <p>****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
Representação de classes sociais pela Globo Filmes é tema de pesquisa na UFSCar
<p>Maxwell Tomaz Assis de Souza, mestrando pelo Programa de Pós Graduação em Imagem e Som (PPGIS - UFSCar), fala de sua pesquisa sobre a representação das classes sociais em comédias produzidas pela Globo Filmes, focando seus estudos na representação da classe média por meio da análise material da cultura de massa.</p> <p>Representação de classes sociais pela Globo Filmes é tema de pesquisa na UFSCar de <a href="https://youtu.be/Z42Cre27404">https://youtu.be/Z42Cre27404</a> está licenciado com uma Licença <a href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons - Atribuição-NãoComercial-SemDerivações 4.0 Internacional</a>.<br> Podem estar disponíveis autorizações adicionais às concedidas no âmbito desta licença em <a href="https://www.labi.ufscar.br/">https://www.labi.ufscar.br/</a>.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p>
"Eco-social education for trainee teachers. Reflections on international mirror-classes in landscape". Study Dataset
<p>Study Dataset for the research work: "Eco-social education for trainee teachers. Reflections on international mirror-classes in landscape"</p>
Molecular subtypes and dietary patterns in breast cancer patients: a latent class analysis
<p>Molecular subtypes and dietary patterns in breast cancer patients: a latent class analysis</p>
Replication package for the article "Who chooses fast-track programs in mathematics? The role of class origin, ethnicity, and gender among Norwegian lower-secondary students", submitted to European Societies.
<p>This file contains a replication package for the article "Who chooses fast-track programs in mathematics? The role of class origin, ethnicity, and gender among Norwegian lower-secondary students", submitted to European Societies</p> <p>****************************************************************************************************************************************<br> The analyses in this article are of secondary data from Norwegian state registers. We analyzed the data with concession from The Norwegian Data Protection Authority, and under the auspices of a confidentiality agreement with Statistics Norway. All direct identifiers were stripped from the file prior to analysis. Data were handled in such a way as to protect individual’s privacy and confidentiality. This included storing these data on a secure Research Computing server and requiring each researcher working with the data to sign a confidentiality affidavit. Other parties may not access the data because it would violate the concession given by The Norwegian Data Protection Authority, and the confidentiality agreement with Statistics Norway. This prevents us from uploading the data to a public repository. <br> Researchers may apply to get access to such data, and the procedure one needs to follow is described at Statistics Norway’s webpage: <br> https://www.ssb.no/en/data-til-forskning/utlan-av-data-til-forskere<br> </p>
Assessment of Temporomandibular Joint for Occurrence and Severity of Disorders in Adult Cases with Unilateral Cleft Lip and Palate and Non-Cleft Class I Using Helkimo Index
<p>master charts</p>
Figure 3. Bayesian phylogenetic reconstruction for MHC class II in Diversity of MHC class II DRB alleles in the Eurasian population of the least weasel, Mustela nivalis (Mustelidae: Mammalia)
Figure 3. Bayesian phylogenetic reconstruction for MHC class II DRB alleles from Mustela nivalis and other species in Mustelidae, Felidae, and Canidae. Numbers near nodes are posterior probability values. Sequences from M. nivalis obtained in this study are in bold. GenBank accession numbers of previously published nucleotide sequences are in parentheses. Clades in Mustelidae are indicated by capital letters (A–G) to the right of the tree. The scale bar at the bottom shows branch length in substitutions per site. Abbreviations for species names are as follows: Cafa, Canis familiaris; Cala, Canis latrans; Calu, Canis lupus; Enlu, Enhydra lutris; Febi, Felis silvestris bieti; Feca, Felis catus; Fesi, Felis silvestris; Gugu, Gulo gulo; Meme, Meles meles; Muit, Mustela itatsi; Mulu, Mustela lutreola; Musi, Mustela sibirica; Nevi, Neovison vison; Tata, Taxidea taxus; Zaca, Zalophus californianus.
Figure 2 in Diversity of MHC class II DRB alleles in the Eurasian population of the least weasel, Mustela nivalis (Mustelidae: Mammalia)
Figure 2. Amino acid sequences deduced from the nucleotide sequences of part of Mustela nivalis MHC class II DRB exon 2. Identity with allele Muni-DRB*01 is indicated by dots. Numbers above the top sequence indicate codon positions in the β1-domain. Grey shading shows the antigen-binding site (ABS) predicted from data on the human gene HLA-DR1 (Brown et al., 1993). Asterisks indicate codons corresponding to sites under positive selection calculated from the mixed-effects model evolution (MEME) analysis. A triangle shows the recombination break point with the single break point recombination (SBP) method (see the text).
Figure 1 in Diversity of MHC class II DRB alleles in the Eurasian population of the least weasel, Mustela nivalis (Mustelidae: Mammalia)
Figure 1. Map of Eurasia showing collecting localities (black circles) for 35 specimens of Mustela nivalis in this study. The sample size at each locality is indicated in parentheses.
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p> <p> </p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This zip file is the artifact that contains the following:</p> <p>1. The binary. </p> <p>2. All classes used in the experiments.</p> <p>3. The experimental data.</p> <p> </p>
Synthetic Histology: Videos of Class Interpolation
<p>Supplementary information for the manuscript titled "Deep Learning Generates Synthetic Cancer Histology for Explainability and Education". These are videos of synthetic histology generated by a conditional generative adversarial network (cGAN), trained on thyroid cancer images and conditioned on whether the image is from a tumor with BRAF-like or RAS-like gene expression. The videos show class interpolation for a given seed, starting with a synthetic histology image of a BRAF-like tumor and transitioning to an image of a RAS-like tumor. These videos illustrate the histologic manifestation of BRAF-RAS gene expression in thyroid tumors using synthetic histology.</p>
Classes in ftgo-microservice and VAE-C decomposition
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using a variational autoencoder with C-means (VAE-C). </p>
Classes in ftgo-microservice and AE-C decomposition
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with C-means (AE-C). </p>
Classes in ftgo-microservice and AE-K decomposition
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with K-means (AE-K). </p>
DEWEYS MODIFICATION FOR ANGLE'S CLASS I MALOCCLUSION – REVISITED
<p>This consists of dataset of the article</p>
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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.