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35 results for “codex”
The Heber-Serrure codex (Ghent, University Library, Ms. 1374)
<p><strong>The Heber-Serrure codex (Ghent, University Library, Ms. 1374)</strong></p> <p>This repository holds the raw XML data underlying the diplomatic edition of the Heber-Serrure codex (Ghent, University Library, Ms. 1374), a Middle Dutch miscellany, dating to the late fourteenth century. The edition was published in the series "Middelnederlandse verzamelhandschriften", under the auspices of the series' editorial panel. The present, digital edition follows the (TEI-inspired) MVN-guidelines developed by Peter Boot and Herman Brinkman, supported by a publicly available Oxygen framework (<a href="https://github.com/HuygensING/mvn-xml">Github</a>). The edition and the (Dutch-language) introduction can be consulted <a href="https://hbsr.mvn.huygens.knaw.nl/">online</a> (additionally archived through the <a href="https://web.archive.org/web/20230928080836/https://hbsr.mvn.huygens.knaw.nl/">Wayback Machine</a>). A IIIF-compliant, open-access facsimile of the manuscript can be consulted through the <a href="https://lib.ugent.be/catalog/rug01:000763342">website</a> of Ghent University Library. The material in this repository is shared under an open access-license (Creative Commons; CC-BY-SA 4.0) that encourages re-use but requires an explicit attribution. If you use this edition, please provide an appropriate scholarly citation, e.g.:</p> <blockquote> <p>Renée Gabriël & Mike Kestemont (eds). De Heber-Serrurecodex: Gent, Universiteitsbibliotheek, Hs. 1374. Diplomatische editie bezorgd door Renée Gabriël en Mike Kestemont, met een dialectologische analyse door Amand Berteloot. Middeleeuwse Verzamelhandschriften uit de Nederlanden XVII. Amsterdam, Huygens Instituut voor Nederlandse Geschiedenis en Cultuur van de Koninklijke Nederlandse Akademie van Wetenschappen, 2023. URL: hbsr.mvn.huygens.knaw.nl. DOI: 10.5281/zenodo.8385501.</p> </blockquote> <p><strong>English summary</strong><br> The Heber-Serrure manuscript (Ghent, University Library, Ms. 1374) is a miscellany containing Middle Dutch rhyming texts, mostly ethical and didactic in content. Although the manuscript is not explicitly dated or localized, there is ample reason to assume that the codex was compiled near the end of the fourteenth century in the Carthusian monastery of Herne (about 18 miles southwest of Brussels). For a variety of reasons, this codex deserves our attention (and a new, modern edition), as it continues to fascinate both philologists and book historians.</p> <p>Until now, the Heber-Serrure manuscript has been primarily valued because of the many unique texts which it contains, including sizable excerpts from the <em>Spiegel historiael</em> (the Middle Dutch adaption of Vincent of Beauvais’ <em>Speculum historiale</em>) as well as a number of rare strophic poems by Jacob van Maerlant, but also the <em>Rinclus</em>. All of these works have already been edited in the past, based on the Heber-Serrure codex. These historic editions, however, were often heavily critical in orientation and appeared in isolation from one another, thus hindering our view on the joint survival of these works, as well as the original context in which this book was produced and meant to function. The present diplomatic edition aims to correct this situation.</p> <p>From the point of book history too, the Heber-Serrure manuscript present us with a remarkable object for scholarly study: the manuscript only contains rhyming texts, but these have been copied as continuous prose, most likely to save space (and time). Moreover, the available evidence suggests that the text collection wasn’t copied from a prior witness: in this manuscript, we can almost literally peak over the scribe’s shoulder, because we are dealing with a ‘growth miscellany’ that was composed in distinct phases, even though these phases were not meticulously planned beforehand. The single scribe of the book also acted as the book’s compiler, thus enabling privileged insights into the dynamic process that led to the gradual expansion of the codex’s content.</p> <p>That we can place the composition of the Heber-Serrure manuscript relatively precisely (in Herne) is unusual for a vernacular medieval codex in the medieval Low Countries. A such, we are able to study the codex in relation to a large number of contemporary sources that were produced in the same monastic environment. The manuscript’s main and only scribe is currently known under the pen name ‘Speculum scribe’, named so after his most famous copy, the second part of the Middle Dutch <em>Speculum historiale</em> adaptation (<em>Spiegel historiael</em>) in Vienna, Ö.N.B. Cod. 13.708; the scribe’s historic identity has not been established (yet), although a large number of manuscripts survive in his handwriting.</p>
Human kidney cortex CODEX reference dataset 1
<p>CODEX image stack of human kidney cortex stained with markers as indicated in CODEX_antibody_list_010621.csv and imaged in the order given in CODEX_channel_index_010621.csv. Tissue preparation and analysis as described <a href="https://www.biorxiv.org/content/10.1101/2021.12.27.474025v1">here.</a></p>
CODEX Rb and Sr isotopic data of granites
<p>The files in this archive represent Rb and Sr isotopic compositions of hundreds of spot analyses on the Boulder Creek Granite and the Pikes Peak Granite. Data were acquired at the Southwest Research Institute in Boulder, Colorado, USA in July-August 2019, using the breadboard version of CODEX, the Chemistry, Organics, and Dating Experiment.</p> <p>The files are in plain text format. Following a one-row header that gives column headings, subsequent rows contain:<br> (1) A unique identifier for each spot analysis;<br> (2-4) 3 columns representing the position of the analysis spot (in mm) relative to a fiducial spot on the sample holder (this permits the construction of an isotopic map of the sample);<br> (5-7) the isotopic abundances for Rb87, Sr86, and Sr87 in units of V*ns, i.e., the product of peak height and duration;<br> (8-10) the 1-sigma uncertainties in each of those isotope abundances, in the same units;<br> (11-13) the correlation coefficients (dimensionless) between fluctuations in the isotope pairs (Rb-87,Sr-86), (Sr-86,Sr-87), (Rb-87,Sr-87). These are important because the uncertainties in the isotope abundances are strongly correlated with one another, making the uncertainties in their ratios smaller than the uncertainty in individual abundances might suggest; <br> (14-15) the ratio of Rb87/Sr86 and its 1-sigma uncertainty; <br> (16-17) the ratio of Sr87/Sr86 and its 1-sigma uncertainty; and<br> (18) the correlation coefficient between the Rb87/Sr86 ratio and the Sr87/Sr86 ratio.</p>
Multispectral Images of the Euchologium Sinaiticum, Pars Nova (Codex Sin. Slav. NF 1)
<p>Multispectral Images of the Euchologium Sinaiticum, Pars Nova (Codex Sin. Slav. NF 1), acquired in St. Catherine's Monastery, Egypt, in 2007. Detailled descriptions of image acquisition and usage are found in<em> NF1_Images_Documentation.pdf</em>.</p>
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
<p>This repository hosts the <strong>relational-only part</strong> of the CoDEx benchmark, which was presented at the EMNLP 2020 conference. You can access the paper <a href="https://www.aclweb.org/anthology/2020.emnlp-main.669.pdf">here</a> and the full dataset, including text and pretrained models, <a href="https://bit.ly/2EPbrJs">on GitHub</a>.</p> <p>Abstract:</p> <p><em>We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available <a href="https://bit.ly/2EPbrJs">here</a>.</em></p>
Processed single cell data from CODEX multiplexed imaging of the human intestine
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on 64 sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006, B008, B009, B010, B011, and B012) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. The outputs of this process were data frames of 2.6 million cells with 57 antibody fluorescence values quantified from each marker. Each cell has its cell type, cellular neighborhood, community of neighborhooods, and tissue unit defined with x, y coordinates representing pixel location in the original image. This is from a total of 25 cell types, 20 multicellular neighborhoods, 10 communities of neighborhoods, and 3 tissue segments that could be used to understand the cellular interactions, composition, and structure of the human intestine from the duodenum to the sigmoid colon and understand differences between different areas of the intestine. This data could be used as a healthy baseline to compare other single-cell datasets of the human intestine, particularly multiplexed imaging ones. </p> <p>The overall structure of the datasets is individual cells segmented out in each row. Columns MUC2 through CD161 are the markers used for clustering the cell types. These are the columns that are the values of the antibody staining the target protein within the tissue quantified at the single-cell level. This value is the per cell/area averaged fluorescent intensity that has subsequently been z normalized along each column as described above. OLFM4 through MUC6 were captured in the quantification but not used within the clustering of cell types. Other columns are explained in the table in the Usage Notes section below.</p> <p>Along with this main data table, there is also a donor metadata table that links the donor ids to clinical metadata such as: age, sex, race, BMI, history of diabetes, history of cancer, history of hypertension, and history of gastorintestinal disease.</p> <p>The raw imaging data can be found at (<a href="https://portal.hubmapconsortium.org/">https://portal.hubmapconsortium.org/</a>). We have created a landing page with links to all the raw dataset IDs and the HuBMAP ID for this Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356. This can be used to also pair it with the matched snRNAseq and snATACseq for each section of tissue.</p>
Dataset: CODEX highly multiplexed tissue imaging in pancreas
<p><strong>Human pancreas</strong></p> <p>This dataset was acquired using CODEX, multiplexed single-cell imaging technology for spatial profiling, where all image data is in .tif format and it includes an associated imaging metadata .csv file. The combination of the targets present in this experiment define some of the main cell types and anatomical structures in human pancreas tissue.</p> <p>This dataset is a 12-highly multiplexed experiment performed on a human pancreas 5 μm section including the nuclear marker Hoechst and antibodies conjugated with oligo-sequences directed against the individual markers. Images were acquired using a Leica DMi8 widefield microscope, a digital CMOS camera (Hamamatsu, ORCA-Flash4.0 V3), and a 20x (0.75) NA dry objective. The light source was a SOLA-SM-II. All images were captured at a 16-bit depth with the following dimensions: x (0.325 μm), y (0.325 μm), and z (1.5 μm). In addition, images were processed, tiled and merged using the CODEX® Processor application (CODEX Processor 1.7.0.6).</p>
The Codex | Researching Islam | iBrary
<p>This video introduces the module “The Codex” from the course Researching Islam, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbDNhVS1VeXhBMUd6WUhHMU50RHZCdkFGeFZEUXxBQ3Jtc0ttTlc3UHhxQ1RfWnhhd2dWc0ZBdGV5NHV2VkkxcXozQ29LRndUdC1jUnVpOGtPaWNJWkNQUk1DQWpvMVJYY0d4eEpSM004SjlCYkRreFU0MjFPcllQWjNGWXU1N1k3YmZvQk9PUldUWXFoME5NdWFrVQ&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=i_u0iLA9wb4">https://www.ecampusontario.ca/</a>.</p>
Human intestine processed CODEX multiplexed images for donors B004-6, B008 (Part 1/2)
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Human intestine processed CODEX multiplexed images for donors B009-B012 (Part 2/2)
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Processed single cell data from CODEX multiplexed imaging of the human intestine
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Antibody panel used for multiplexed antibody-based imaging of Human Pancreas Analysis Program (HPAP) samples by CODEX
<p>This data file details antibodies applied to human pancreas tissue samples from the Human Pancreas Analysis Program (HPAP; RRID:SCR_016202) of the <a href="https://hirnetwork.org/">Human Islet Research Network</a> (HIRN; RRID:SCR_014393). Images will be uploaded for interactive analysis on <a href="https://pancreatlas.org/datasets">Pancreatlas</a> (RRID:SCR_018567) and made available for download via <a href="https://hpap.pmacs.upenn.edu/">PANC-DB</a>. Workflow is documented on protocols.io: <a href="https://dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1">dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1</a>.</p><p>Table format adapted from Radtke AJ, Quardokus EM, Saunders DC (2022), <a href="https://doi.org/10.5281/zenodo.7386417">SOP: Construction of Organ Mapping Antibody Panels for Multiplexed Antibody-Based Imaging of Human Tissues</a>. See also: Saunders D; Reihsmann R. <a href="https://doi.org/10.48539/HBM754.BHVR.258">OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0</a>.</p>
CODEX multiplexed imaging of immunotherapy in human and mouse melanomas
<p>Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into melanoma tumors in both murine models and human samples. CODEX imaging involves an iterative process of annealing and stripping fluorophore-labeled oligonucleotide barcodes, complementing the barcodes attached to over 40 antibodies used for tissue staining. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue.</p> <p>Our datasets comprise individual cells as rows, each characterized by 40+ antibody fluorescence values quantified from various markers evaluated for each study. These markers correspond to the antibodies targeting specific proteins within the tissue, quantified at the single-cell level. The values represent per-cell/area-averaged fluorescent intensities, z-normalized along each column. Each cell is mapped with its cell type and cellular neighborhood, defined by x and y coordinates representing pixel locations in the original image. Refer to the table in the "Usage Notes" section below for further details. The CODEX multiplexed imaging data is organized into three distinct files, each representing key aspects of our research and the studies detailed in our manuscript.</p> <p>We then used this data investigate the major cellular organization of the tumor sections we imaged, with downstream spatial statistics and analyses like cellular neighborhood analysis and cell-cell interaction analysis. These data could be used to understand the cellular interactions, composition, and structure of anti-tumor melanoma responses induced by antigen-specific immunotherapy either with adoptive T cell transfer for checkpoint blockade immunotherapy. These datasets offer valuable insights for researchers interested in anti-tumor microenvironments, immune responses, and therapeutic interventions such as T cell therapies.</p> <p><em>1. Time-course of tumor microenvironment following antigen-specific T cell therapy in mice</em></p> <p>We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and tumor progression in a murine melanoma model. We activated PMEL CD8+ T cells with cognate antigen gp100 and IL-2 for 10 days ex vivo and transferred into mice with established B16-F10 tumors. Tumors were harvested and imaged with CODEX imaging at 0-, 1-, 3-, 5-, and 12-days post-treatment (n=3-7 per time point). Our 42-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, stromal cell types, and tumor cell phenotypes, resulting in a rich dataset of 1,052,125 cells across 42 marker channels.</p> <p><em>2. Tumor microenvironment following antigen-specific T cell therapies with different phenotypes in mice</em></p> <p>We delve deeper into the modulation of the tumor microenvironment by manipulating T cell phenotypes. By comparing activated T cells stimulated with and without 2-hydroxycitrate (2HC), a metabolic inhibitor of acetyl CoA production, we explore the impact of phenotype on tumor progression. Our datasets from mice treated with 2HC T cells or T cells provide insights into the role of T cell phenotype manipulation in the tumor microenvironment (n=4-7 per group).</p> <p><em>3. Tumor microenvironment before and after checkpoint blockade in human melanoma patients of both responders and non-responders</em></p> <p>Our research extends to human melanoma patients with advanced, metastatic, stage IV tumors. We examine 12 FFPE tumor samples from six patients, each with samples taken before and after checkpoint inhibitor therapy. Our CODEX multiplexed imaging, using a panel of 58 antibodies, reveals changes in immune, stromal, and tumor compartments. We segmented 5,019,159 individual cells from the 12 CODEX images, facilitating unsupervised clustering to identify 39 major cell types based on their expression profiles. Our accompanying donor metadata table links donor IDs to essential clinical information, including treatment response, demographics, and sample details.</p>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Detail 01 on folio 19v of the 8th century Codex Eyckensis
<p>Detail 01 on folio 19v of the 8th century Codex Eyckensis, parchment (© Musea Maaseik) – (<strong>A</strong>) Phase One RGB image acquisition according to Metamorfoze standard; (<strong>B-D</strong>) Renderings based on one and the same 5min recording with the MS PLD system; (<strong>B</strong>) False color (IR-Red-Green) differentiating and accentuation the red lead pigment, PLD rendering further enhanced with color balance in Adobe Photoshop CC 2015.5; (<strong>C</strong>) IR image visualizing additions/corrections in different ink; (<strong>D</strong>) Exaggerated Shaded image with measurements of the parchment surface relief’s section at multiple locations (© Codex Eyckesis Project & KU Leuven)</p>
Detail 02 on folio 19v of the 8th century Codex Eyckensis
<p>Detail 01 on folio 19v of the 8th century Codex Eyckensis, parchment (© Musea Maaseik) – (<strong>A</strong>) Phase One RGB image acquisition according to Metamorfoze standard; (<strong>B-D</strong>) Renderings based on one and the same 5min recording with the MS PLD system; (<strong>B</strong>) False color (IR-Red-Green) differentiating and accentuation the red lead pigment, PLD rendering further enhanced with color balance in Adobe Photoshop CC 2015.5; (<strong>C</strong>) IR image visualizing additions/corrections in different ink; (<strong>D</strong>) Exaggerated Shaded image with measurements of the parchment surface relief’s section at multiple locations (© Codex Eyckesis Project & KU Leuven)</p>
CODEX: Oscillatory brain activity during acute exercise: Tonic and transient neural response to an Oddball task.
<p><strong>2. Method</strong></p> <p><em>2.1.</em> <em>Participants</em></p> <p>We recruited 20 young males with a high level of aerobic fitness (age between 18-31 years old, average age 23.9 years old) from the University of Granada (Spain). All participants met the inclusion criteria of reporting at least 8 hours of cycling or triathlon training per week, normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders and were taking no medication. Note that high-fit cyclists and triathletes were selected because they are capable of maintaining a pedalling cadence at moderate-to-high intensity during long periods of time. Furthermore, they are able to keep a fixed posture over time, which reduces EEG movement artifacts considerably. Their fitness level was verified by an incremental effort test (see below). Participants were required to maintain a regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense physical activity 24 hours before each session. All subjects gave written informed consent before the study. The protocol was in accordance with both, the ethical guidelines of the University of Granada, and the Declaration of Helsinki.</p> <p> </p> <p><em>2.2. Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Oddball stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 100 cm between the head of participants and the centre of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p> </p> <p><em>2.3. Fitness Assessments</em></p> <p>Participants came to the laboratory at least one week before the first experimental session to provide the informed consent, complete an anthropometric evaluation (height, weight and body mass index [BMI]) and to familiarize with the oddball task. Subsequently, they performed an incremental cycle-ergometer test to obtain their VO<sub>2max</sub> that was used in the following experimental sessions to adjust the exercise intensity individually. The incremental effort test started with a 3 minutes warm-up at 30 Watts (W), with the power output increasing 10 W every minute. Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) during the warm-up period and was asked to maintain this cadence during the entire protocol. The test began at 60 W and was followed by an incremental protocol of 30 W every 3 minutes. Each step of the incremental protocol consisted of 2 minutes of stabilized load and 1 minute of progressive load increase (5 W every 10 seconds). The oxygen uptake (VO<sub>2</sub> ml • min<sup>-1 </sup>• kg<sup>-1</sup>), respiratory exchange ratio (RER; i.e., CO<sub>2</sub> production • O<sub>2</sub> consumption<sup>-1</sup>), relative power output (W • Kg<sup>-1</sup>) and heart rate (bpm) were continuously recorded throughout the test.</p> <p> </p> <p><em>2.4. Experimental sessions</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 100 min each. To avoid possible fatigue and/or training effects, visits to the laboratory were scheduled on different days allowing 48–72 hours between sessions. On each experimental session, after 10’ warm-up on a cycle-ergometer at a power load of 30% of their individual VO<sub>2max</sub>, participants performed an oddball task for 20’ while pedalling either at 30% (Light intensity exercise session) or 80% (Moderate-intensity exercise session) of their VO<sub>2max</sub>. Upon completion of the oddball task, a 10’ cool down period at 30% of intensity followed (see Table 1). Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions, as much as possible, in terms of dual-task demands.</p> <p> </p> <p><em>2.5. Oddball task</em></p> <p>The visual oddball task was based on that reported in Sawaki and Katayama (Sawaki & Katayama, 2007). It consisted of a random presentation of three visual stimuli: a frequent small blue circle (approximately 1.15º x 1.15º), a rare big blue circle (approximately 1.30º x 1.30º), and a rare red square (approximately 2.00º x 2.00º). Small blue circles were considered as standard stimuli (non-target), while big blue circles (target 1) and red squares (target 2) were considered as target stimuli. Stimuli were displayed sequentially on the centre of the screen on a black background. Each trial started with the presentation of a blank screen in a black background for 1200 ms. Then, the stimulus was presented at a random time interval (between 0 and 800 ms) during 150 ms. Participants were instructed to respond to both targets by pressing a button connected to the cycle-ergometer handlebar with the thumb of their dominant hand and to not respond when standard stimuli were shown. Participants were encouraged to respond as accurately as possible. The target stimuli were randomly presented in 20% of trials (10% of target 1, 10% target 2) and the non-target stimulus in the remaining 80% of trials. A total of 600 stimuli were presented. The task lasted for 20 minutes approximately. No breaks were allowed.</p> <p> </p> <p><em>2.6. EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ) to optimize signal transduction. Participants were instructed to avoid postural movements as much as possible, and to keep their gaze on the centre of the screen during the task. Electrode impedances were kept below 10 kΩ.</p>
Processed CODEX Datasets from - Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens
<p>This entry provides access to processed CODEX data files of three studies analyzed in the article "Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens". Details of datasets can be found in the Methods section of the article.</p> <p>For each dataset:</p> <ul> <li>A comma-separated values (CSV) file containing metadata of regions is included</li> <li>A zip file containing multiple CSV files is included: <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified and normalized for all cells in this region.</li> <li>`{region_id}.cell_types.csv`, a table containing two columns: "CELL_ID" and "CELL_TYPE". This table provides cell type annotations for all cells in this region.</li> <li>`{region_id}.cell_features.csv`, a table containing two columns: "CELL_ID" and "SIZE". This table provides morphology descriptors (only containing cell size for these studies) for all cells in this region.</li> </ul> </li> </ul> <p>These data files are also available through the Enable Medicine Public Study page: <a href="https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168">https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168</a>. Raw multiplexed immunofluorescence images will be accessible through the visualizer app of Enable Medicine Portal.</p> <p>Codes for this study are stored in <a href="https://gitlab.com/enable-medicine-public/space-gm">https://gitlab.com/enable-medicine-public/space-gm</a>. Please direct all further questions and/or issues to the gitlab repository or lead contact (A.E.T.).</p>
Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular.</p> <p>From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 unique cell type labels. We also labeled cell types by hiearchical hand-gating data within cellengine (cellengine.com). We also created another gold standard for comparison by overclustering unormalized data with X-shift angular clustering. Finally, we created one last label as the major cell type call from each cell from all 21 cell type labels in the dataset. </p> <p>Consequently the dataset has individual cells segmented out in each row. Then there are columns for the X, Y position in pixels in the overall montage image of the dataset. There are also columns to indicate which region the data came from (4 total). The rest are labels generated by all the clustering and normalization techniques used in the manuscript and what were compared to each other. These also were the data that were used for neighborhood analysis for the last figure of the manuscript. These are provided at all four levels of cell type level granularity (from 7 cell types to 35 cell types). </p>
Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging
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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.