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7,523 results for “Annotation”
Figure 1A–G. A in Some hydroids (Hydrozoa: Hydroidolina) from Dampier, Western Australia: annotated list with description of two new species.
Figure 1A–G. A. Eudendrium racemosum from photograph of whole colony. B. Filellum serratum, regenerated hydrotheca. C, D. Lafoeina amirantensis. C, small hydrotheca and nematotheca on hydrorhiza. D, large hydrotheca. E–G. Halecium?tenellum. E, unbranched stem. F, basally annulated hydrophore. G, hydrotheca with strongly everted rim and desmocytes. Scale bar, mm: A, 20. B, C, E, F, G, 0.2. D. 0.1.
Figure 4A–E. A in Some hydroids (Hydrozoa: Hydroidolina) from Dampier, Western Australia: annotated list with description of two new species.
Figure 4A–E. A. Plumularia fragilia sp. nov., holotype colony. B, stem internodes and alternate hydrocladia. C, proximal part of hydrocladium and apophysis with axial nematothecae. D, hydrocladium with athecate and thecate internodes. E. Plumularia bedoti. Distal part of hydrocladium with developing anastomose. F. Lytocarpia delicatula. Two hydrocladial hydrothecae. Scale bar, mm: A 10. B, 1.0. C–F, 0.2
FIG. 3 in An annotated list of hornwort and liverwort species of Serbia
FIG. 3. — The graphical representation of total number of hornwort and liverworts present in Serbia in time.
Figure 1 in Bird diversity and annotated checklist of Afrotropical species in extreme south of Algeria
Figure 1. Location of Timiaouine region, Algeria. Figura 1. Ubicación de la región de Timiaouine, Argelia.
Annotation of the non-canonical translatome reveals that CHO cell microproteins are a new class of therapeutic antibody drug product impurity
<p>3,681 novel Chinese hamster proteoforms (derived from uORFs, ouORFs and ORFs encoded on NCBI annotated non-coding RNAs).</p>
Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains some images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_3, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_2, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p>
Nissl_6, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p> <p> </p>
Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Data from: Regulatory genome annotation for 33 insect species
<p>Annotation of newly-sequenced genomes frequently includes genes, but rarely covers important non-coding genomic features such as the cis -regulatory modules—e.g., enhancers and silencers—that regulate gene expression. Here, we begin to remedy this situation by developing a workflow for rapid initial annotation of insect regulatory sequences, and provide a searchable database resource with enhancer predictions for 33 genomes. Using our previously-developed SCRMshaw computational enhancer prediction method, we predict over 2.8 million regulatory sequences along with the tissues where they are expected to be active, in a set of insect species ranging over 360 million years of evolution. Extensive analysis and validation of the data provides several lines of evidence suggesting that we achieve a high true-positive rate for enhancer prediction. One, we show that our predictions target specific loci, rather than random genomic locations. Two, we predict enhancers in orthologous loci across a diverged set of species to a significantly higher degree than random expectation would allow. Three, we demonstrate that our predictions are highly enriched for regions of accessible chromatin. Four, we achieve a validation rate in excess of 70% using in vivo reporter gene assays. As we continue to annotate both new tissues and new species, our regulatory annotation resource will provide a rich source of data for the research community and will have utility for both small-scale (single gene, single species) and large-scale (many genes, many species) studies of gene regulation. In particular, the ability to search for functionally-related regulatory elements in orthologous loci should greatly facilitate studies of enhancer evolution even among distantly related species.</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
<p>Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation.</p>
Cellpose training data and scripts from "Machine learning for histological annotation and quantification of cortical layers"
<p>This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper</p> <p> "<strong>Machine learning for histological annotation and quantification of cortical layers</strong>"</p> <p>Inside this workflow and dataset, you will find the following folders</p> <ol> <li><strong>QuPath Training Project</strong>: A QuPath 0.5.0 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training</li> <li><strong>Training Images</strong> and <strong>Demo Images</strong>: The raw whole slide scanner images needed by the above QuPath project</li> <li><strong>Model</strong>: The fodler containing the trained cellpose model</li> <li><strong>cellpose-training Folder</strong>: The exported raw and ground truth images that the above cellpose model was trained on</li> <li><strong>Scripts</strong>: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow</li> <li><strong>QC</strong>: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics.</li> </ol> <p>Installation and Use</p> <p>If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension https://github.com/BIOP/qupath-extension-cellpose as well as a working Cellpose installation https://github.com/MouseLand/cellpose</p> <p>Instructions for installation are available from the links above.</p> <p>After that, you should be able to open the QuPath project, navigate to the "Automate > Project scripts" menu and locate the script you wish to run.</p> <p><br>1. train a cell segmentation algorithm in the context of the rat brain Layer <br>Boundaries project </p> <p>2. trigger cell segmentation from a QuPath project in a semi-automated pipeline</p>
Fig. 3 in Annotated checklist of the herpetofauna (Amphibia, Reptilia) of Lefkada Island (Ionian Islands, Greece)
Fig. 3 − Herpetofauna observed by the authors on Lefkada. / Erpetofauna osservata dagli autori a Lefkada. A. Ablepharus kitaibelii. B. Malpolon insignitus. C. Natrix natrix. D. Vipera ammodytes. E. Elaphe quatuorlineata.
Fig. 2 in Annotated checklist of the herpetofauna (Amphibia, Reptilia) of Lefkada Island (Ionian Islands, Greece)
Fig. 2 - Herpetofauna observed by the authors on Lefkada. / Erpetofauna osservata dagli autori a Lefkada. A) Bufo bufo tadpoles. B) Pelophylax kurtmuelleri. C) Emys orbicularis. D) Mauremys rivulata. E) Hemidactylus turcicus. F) Pseudopus apodus. G) Algyroides nigropunctatus. H) Lacerta trilineata.
Figs. 13-14 in Annotated checklist of the handsome fungus beetles of Connecticut, USA (Coleoptera: Cucujoidea: Endomychidae).
Figs. 13-14.- Mycetaea subterranea (Fabricius, 1801). 13.- Habitus (Photo by Christoph Benisch-www.kerbtier.de). 14.- Distributional map.
Figs. 3-4 in Annotated checklist of the handsome fungus beetles of Connecticut, USA (Coleoptera: Cucujoidea: Endomychidae).
Figs. 3-4.- Phymaphora pulchella Newman, 1838. 3.- Habitus (Photo by Tom Murray). 4.- Distributional map. Figs. 5-6.- Rhanidea unicolor (Ziegler, 1845). 5.- Habitus (Photo by Tom Murray). 6.- Distributional map.
Fig. 7 in An annotated checklist of the herpetofauna of the Sibiloi National Park in northern Kenya based on field surveys
Fig. 7. Occurrence of the six recorded amphibian species across the survey sites (AB, Fig. 8. Numbers of amphibian species that were exclusively found at either one of the Alia Bay; KF, Koobi Fora; KA, Karare; IL, Ilkemere; LO, Lomosia) and transects (G, surveyed sites or one of the transects. Abbreviations: AB, Alia Bay; KF, Koobi Fora; KA, grassland; R, riverbed; B, bushland; Tot, Total). Karare; IL, Ilkemere; LO, Lomosia; G, grassland; R, riverbed; B, bushland; Tot, Total.
Fig. 5 in An annotated checklist of the herpetofauna of the Sibiloi National Park in northern Kenya based on field surveys
Fig. 5. Occurrence of the 28 recorded reptile species across the survey sites (AB, Alia Fig. 6. Numbers of reptile species that were exclusively found at either one of the surveyed Bay; KF, Koobi Fora; KA, Karare; IL, Ilkemere; LO, Lomosia; TBI, Turkana Basin sites or one of the transects. Abbreviations: AB, Alia Bay; KF, Koobi Fora; KA, Karare; Institute) and by transect (G, grassland; R, riverbed; B, bushland; Tot, Total). IL, Ilkemere; LO, Lomosia; TBI, Turkana Basin Institute; G, grassland; R, riverbed; B, bushland; Tot, Total.
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.