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9,302 results for “DETECT”
Dataset for "Bacterial genome annotation" and "AMR gene detection" workflows
<p>This dataset is associated with the workflows "Bacterial genome annotation" and "AMR gene detection in an assembled bacterial genome".</p>
Putative mutations associated with tetracycline resistance detected in Treponema spp.- an analysis of 4,355 Spirochaetales genomes
<p>Supplementary Table 1–4, , as well as multiple sequence alignment files for 16S rRNA, rpsC, and rpsJ loci, have been deposited.</p> <p>Additionally, the archive contains a <code>scripts.txt</code> file that includes all custom Bash scripts used for:</p> <ul> <li> <p>rRNA gene detection and quantification using <em>Barrnap</em></p> </li> <li> <p>Genome quality assessment using <em>CheckM</em></p> </li> <li> <p>Gene-by-gene schema creation, allele calling, and cgMLST extraction using <em>chewBBACA</em></p> </li> <li> <p>GFF merging and wide-format transformation for summarizing rRNA copy number per genome</p> </li> </ul> <p>These materials are provided to ensure reproducibility of the analyses and to support further investigations into antimicrobial resistance in <em>Spirochaetales</em>.</p>
OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables
<p>Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.</p>
Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila - Processed Data
<p>Processed data and code for plotting figures for the paper:</p><p>"Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila", by Kit D. Longden, Edward M. Rogers, Aljoscha Nern, Heather Dionne, Michael B. Reiser.</p><p>Data (compressed results folder) and plotting code (compressed src folder) are MATLAB files (see READ_ME for version information and toolboxes). The Source Data excel file also contains the data plotted in the paper figures.</p>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
Next-generation 3D object detection and tracking for self-driving vehicles using object velocity
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p><ul><li>Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;</li><li>Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;</li><li>Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;</li><li>Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;</li></ul><p>Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.</p><p>This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.</p>
Synthbuster: Towards Detection of Diffusion Model Generated Images
<p>Dataset described in the paper "Synthbuster: Towards Detection of Diffusion Model Generated Images" (Quentin Bammey, 2023, <i>Open Journal of Signal Processing</i>)</p><p>This dataset contains synthetic, AI-generated images from 9 different models:</p><ul><li>DALL·E 2</li><li>DALL·E 3</li><li>Adobe Firefly</li><li>Midjourney v5</li><li>Stable Diffusion 1.3</li><li>Stable Diffusion 1.4</li><li>Stable Diffusion 2</li><li>Stable Diffusion XL</li><li>Glide</li></ul><p> </p><p>1000 images were generated per model. The images are loosely based on raise-1k images (Dang-Nguyen, Duc-Tien, et al. "Raise: A raw images dataset for digital image forensics." Proceedings of the 6th ACM multimedia systems conference. 2015.). For each image of the raise-1k dataset, a description was generated using the Midjourney /describe function and CLIP interrogator (https://github.com/pharmapsychotic/clip-interrogator/). Each of these prompts was manually edited to produce results as photorealistic as possible and remove living persons and artists names.</p><p> </p><p>In addition to this, parameters were randomly selected within reasonable values for methods requiring so.</p><p>The prompts and parameters used for each method can be found in the `prompts.csv` file.</p><p> </p><p>This dataset can be used to evaluate AI-generated image detection methods. We recommend matching the generated images with the real Raise-1k images, to evaluate whether the methods can distinguish the two of them. Raise-1k images are not included in the dataset, they can be downloaded separately at (http://loki.disi.unitn.it/RAISE/download.html).</p><p> </p><p>None of the images suffered degradations such as JPEG compression or resampling, which leaves room to add your own degradations to test robustness to various transformation in a controlled manner.</p><p> </p>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
<p>This dataset contains detections of echolocation clicks at two sites in the Hawaiian Archipelago. These sites are Hawaii and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into five-minute bins and each bin was given a class label. The data provided here is further binned at a daily level, where counts of a given class represent the number of five-minute bins within a given day that were labeled as that class. One file is provided per site, and files are in .csv format that can be read using any desired coding language. </p> <p>In addition to acoustic counts, values for environmental variables considered in the corresponding manuscript are provided in the CSV files. The final file in this dataset contains satellite-derived chlorophyll-a concentration values from NASA MODIS for the Hawaiian region (used to create Supplementary Fig. 1). Details on all variables and how they were accessed can be found in the manuscript and in the README file accompanying this dataset. </p>
Heterogeneous/Homogeneous Change Detection dataset
<p><i><strong>"Please if you use this datasets we appreciated that you reference this repository and cite the works related that made possible the generation of this dataset."</strong></i></p><p>This change detection datastet has different events, satellites, resolutions and includes both homogeneous/heterogeneous cases. The main idea of the dataset is to bring a benchmark on semantic change detection in remote sensing field.<br><br>This dataset is the outcome of the following publications:</p><ol><li>@article{ JimenezSierra2022graph,<br>author={Jimenez-Sierra, David Alejandro and Quintero-Olaya, David Alfredo and Alvear-Mu{\~n}oz, Juan Carlos and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Florez-Ospina, Juan Felipe and Chanussot, Jocelyn},<br>journal={IEEE Transactions on Geoscience and Remote Sensing},<br>title={Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection},<br>year={2022},<br>volume={60},<br>number={},<br>pages={1-16},<br>doi={10.1109/TGRS.2022.3168126}<br>}<br> </li><li>@article{ JimenezSierra2020graph,<br>title={Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops},<br>author={Jimenez-Sierra, David Alejandro and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Vargas-Cardona, Hern{\'a}n Dar{\'i}o and Chanussot, Jocelyn},<br>journal={Remote Sensing},<br>volume={12},<br>number={17},<br>pages={2683},<br>year={2020},<br>publisher={Multidisciplinary Digital Publishing Institute},<br>doi={10.3390/rs12172683}<br>}<br> </li><li>@inproceedings{jimenez2021blue,<br>title={Blue noise sampling and Nystrom extension for graph based change detection},<br>author={Jimenez-Sierra, David Alejandro and Ben{\'\i}tez-Restrepo, Hern{\'a}n Dar{\'\i}o and Arce, Gonzalo R and Florez-Ospina, Juan F},<br>booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},<br>ages={2895--2898},<br>year={2021},<br>organization={IEEE},<br>doi={10.1109/IGARSS47720.2021.9555107}<br>}<br> </li><li>@article{florez2023exploiting,<br>title={Exploiting variational inequalities for generalized change detection on graphs},<br>author={Florez-Ospina, Juan F and Jimenez Sierra, David A and Benitez-Restrepo, Hernan D and Arce, Gonzalo},<br>journal={IEEE Transactions on Geoscience and Remote Sensing}, <br>year={2023},<br>volume={61},<br>number={},<br>pages={1-16},<br>doi={10.1109/TGRS.2023.3322377}<br>}<br> </li><li>@article{florez2023exploitingxiv,<br>title={Exploiting variational inequalities for generalized change detection on graphs},<br>author={Florez-Ospina, Juan F. and Jimenez-Sierra, David A. and Benitez-Restrepo, Hernan D. and Arce, Gonzalo R},<br>year={2023},<br>publisher={TechRxiv},<br>doi={10.36227/techrxiv.23295866.v1}<br>}</li></ol><p>In the table on the html file (<a href="https://zenodo.org/records/8269855/files/dataset_table.html?download=1">dataset_table.html</a>) are tabulated all the metadata and details related to each case within the dasetet. The cases with a link, were gathered from those sources and authors, therefore you should refer to their work as well.</p><p>The rest of the cases or events (without a link), were obtained through the use of open sources such as:</p><ul><li><a href="https://scihub.copernicus.eu/dhus/#/home">Copernicus</a></li><li><a href="https://esar-ds.eo.esa.int/oads/access/collection">European Space Agency</a></li><li><a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (Vertex)</a></li><li><a href="https://search.earthdata.nasa.gov/search">Earth Data</a></li></ul><p>In addition, we carried out all the processing of the images by using the <a href="https://github.com/senbox-org/snap-desktop/tree/master">SNAP toolbox</a> from the European Space Agency. This proccessing involves the following:</p><ul><li>Data co-registration</li><li>Cropping</li><li>Apply Orbit (for SAR data)</li><li>Calibration (for SAR data)</li><li>Speckle Filter (for SAR data)</li><li>Terrain Correction (for SAR data)</li></ul><p>Lastly, the ground truth was obtained from homogeneous images for pre/post events by drawing polygons to highlight the areas where a visible change was present. The images where layout and synchorized to be zoomed over the same are to have a better view of changes. This was an exhaustive work in order to be precise as possible.<br><br>Feel free to improve and contribute to this dataset.</p>
Simulated Detection and Localization in LIGO's HL-Configuration for O4a Run, and LIGO/Virgo/KAGRA's HLVK-Configuration for O4b and O5 Runs (November 2023 edition)
<p>We have conducted a simulation of the <strong>HL</strong>-configuration deployed during the ongoing <strong>O4a</strong> run and the <strong>HLVK-</strong>configuration planned for <strong>O4b </strong>and O5, as outlined in the observation scenarios, which can also be accessed at the following <a href=" https://doi.org/10.5281/zenodo.7026209"> https://doi.org/10.5281/zenodo.7026209. </a></p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
Replication Package for: Detecting Edgeworth Cycles (Version 2)
<p>This replication package contains the <strong>data </strong>and the <strong>code </strong>to generate the main results reported in <strong>"Detecting Edgeworth Cycles"</strong> by <strong>Timothy Holt, Mitsuru Igami, and Simon Scheidegger</strong>, to be published in the February 2024 issue of <em>The Journal of Law and Economics</em>. </p> <p>Additionally, this package also allows the users to<strong> apply these pre-trained models to new datasets</strong> of their choice. As an example of such a new dataset, we include the <strong>entire German dataset available at the time of our research (2014:Q4–2020:Q4)</strong>, including both manually labeled and unlabeled subsamples.</p> <p>Finally, this package includes <strong>tools to facilitate the acquisition and pre-processing of the most recent data from Germany</strong>, which is updated every day on the <em>Tankerkoenig</em> website (at the time of our preparation of this package).</p> <p><strong>[3/29/2024 update]</strong> The URL for the German antitrust authority's fuel-data website has changed to <a href="https://www.bundeskartellamt.de/EN/Tasks/markettransparencyunit_fuels/markettransparencyunit_fuels.html" target="_blank" rel="noopener">https://www.bundeskartellamt.de/EN/Tasks/markettransparencyunit_fuels/markettransparencyunit_fuels.html</a>.</p>
Organic electrochemical transistor aptasensor for interleukin-6 detection
<p>Explanation of included data for following publication:</p><p>-------------------------------------------------------------------------------------</p><p><strong>Organic electrochemical transistor aptasensor for interleukin-6 detection</strong></p><p>Chiara Diacci (1,2), Bernhard Burtscher (1), Marcello Berto (2), Tero Petri Ruoko (1), Samuel Lienemann (1), Pierpaolo Greco (3,4), Magnus Berggren (1), Marco Borsari (5), Daniel T. Simon (1), Carlo A. Bortolotti (2), Fabio Biscarini (2,4)</p><p> </p><p>1) Laboratory of Organic Electronics, Department of Science and Technology, ITN, Linköping University, 601 74, Norrköping, Sweden.</p><p>2) Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, via Campi 103, 41125 Modena, Italy.</p><p>3) Department of Neuroscience and Rehabilitation, Università di Ferrara, Via Borsari 46, 44121 Ferrara, Italy.</p><p>4) Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, via Fossato di Mortara 17-193, 44100 Ferrara, Italy.</p><p>5) Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, via Campi 103, 41125 Modena, Italy.</p><p>-------------------------------------------------------------------------------------</p><p><strong>Manuscript</strong></p><p> </p><p>Figure 2:</p><ul><li>SEM pictures</li></ul><p> </p><p>Figure 3:</p><ul><li>CV for aptamer deposition (Time, Voltage, Current) in 1µM aptamer solution from 0V to -1V, 50mV/s for 30 cycles</li><li>Impedance data for AuNP/PEDOT:PSS electrode (before aptamer deposition) and after aptamer deposition (Aptamer/AuNP/PEDOT:PSS); (Freq/Hz, Re(Z)/Ohm, -Im(Z)/Ohm, |Z|/Ohm, Phase/deg) in ferricyanide 5mM</li></ul><p> </p><p>Figure 4: Fluorescent images (LSM, as czi format) for</p><ul><li>4a: AuNP/PEDOT:PSS (no Cy3-tag, control)</li><li>4b: Cy3-aptamer/AuNP/PEDOT:PSS (sample)</li><li>4c: Cy3-aptamer/PEDOT:PSS (no AuNP, control)</li></ul><p> </p><p>Figure 5: Data sets of transfer curves for multiple OECTs for various concentrations of IL6/TNF</p><ul><li>One sheet refers to one OECT sample with either IL6 or TNF</li><li>The first coloumn indicates the gate voltage and the others the drain current with various concentration except the second column (Tween) with is without any analyte</li><li>VDS = -0.3V</li><li>VGS swept from 0 to 0.6V</li></ul><p> </p><p> </p><p> </p><p>Supporting Information:</p><p> </p><p>Figure S1: AuNP/PEDOT:PSS electrical characteristics</p><ul><li>S1a: CV in 5mM ferricyanide from -0.5V to 0.5V at 50mV/s</li><li>S1b: Transconductance for bare Au, PEDOT:PSS in PBS 10mM</li><li>S1c: Transfer curves in PBS 10mM</li></ul><p> </p><p>Figure S2: FTIR-ATR measurements</p><ul><li>Raw exported files from the FTIR (Au+PEDOT, Au+DNA, Au+PEDOT+NP, Au+PEDOT+NP+DNA) as txt file.</li><li>For Au+DNA the normalized with the maximum and for the others the 1010cm-1 peak used for normalization. This is shown in the ATR.xlsx file.</li></ul><p> </p><p>Figure S3: IL6 detection on AuNP/PEDOT:PSS gate</p><ul><li>Transfer Charasteristics of the AuNP/PEDOT:PSS electrode with increasing IL6 concentration with VDS=-0.3V and VGS from 0V to 0.6V</li></ul><p> </p><p>Figure S4: Transconductance of OECT based aptasensor</p><ul><li>Datasets used for S4 are identical with the ones from Figure 5</li></ul><p> </p><p>Figure S5: Impedance of functionalized device with IL6</p><ul><li>Each sheet is for a IL6 concentration. The normalized OECT concentration is the same as in Figure 5.</li></ul>
Preliminary Mitosis Detection Results for TCGA-BRCA Dataset
<p>This dataset provides mitosis detection results employing the "Mitosis Detection, Fast and Slow" (MDFS) algorithm [<a href="https://arxiv.org/abs/2208.12587">[2208.12587] Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures (arxiv.org)</a>] on the TCGA-BRCA dataset. </p> <p>The MDFS algorithm exemplifies a robust and efficient two-stage process for mitosis detection. Initially, potential mitotic figures are identified and later refined. The proposed model for the preliminary identification of candidates, the EUNet, stands out for its swift and accurate performance, largely due to its structural design. EUNet operates by outlining candidate areas at a lower resolution, significantly expediting the detection process. In the second phase, the initially identified candidates undergo further refinement using a more intricate classifier network, namely the EfficientNet-B7. The MDFS algorithm was originally developed for the MIDOG challenges.</p> <p> </p> <h2>Viewing in QuPath</h2> <p>The dataset at hand comprises GeoJSON files in two categories: mitosis and proxy (mimicker -- the candidates that are unlikely to be mitosis based on our algorithm). Users can open and visualize each category overlaid on the Whole Slide Image (WSI) using QuPath. Simply drag and drop the annotation file onto the opened image in the program. Additionally, users can employ the provided Python snippet to read the annotation into a Python dictionary or a Numpy array.</p> <p> </p> <h2>Loading in Python</h2> <p>To load the GeoJSON files in Python, users can use the following code:</p> <blockquote> <p>import json</p> <p>import numpy as np</p> <p>import pandas as pd</p> <p> </p> <p>def load_geojson(filename):</p> <p> # Load the GeoJSON file</p> <p> with open(filename, 'r') as f:</p> <p> data = json.load(f)</p> <p> </p> <p> # Extract the properties and store in a dictionary</p> <p> slide_properties = data["properties"]</p> <p> </p> <p> # Convert the points to a numpy array</p> <p> points_np = np.array([(feat['geometry']['coordinates'][0], feat['geometry']['coordinates'][1], feat['properties']['score']) for feat in data['features']])</p> <p> </p> <p> # Convert the points to a pandas DataFrame</p> <p> points_df = pd.DataFrame(points_np, columns=['x', 'y', 'score'])</p> <p> </p> <p> return slide_properties, points_np, points_df</p> <p> </p> <p> </p> <p># Use the function to load mitosis data</p> <p>mitosis_properties, mitosis_points_np, mitosis_points_df = load_geojson('mitosis.geojson')</p> <p> </p> <p># Use the function to load mimickers data</p> <p>mimickers_properties, mimickers_points_np, mimickers_points_df = load_geojson('mimickers.geojson')</p> </blockquote> <h2>Properties</h2> <p>Each WSI in the dataset includes the candidate's centroid, bounding box, hotspot location, hotspot mitotic count, and hotspot mitotic score. The structures of the mitosis and mimicker property dictionaries are as follows:</p> <p>Mitosis property dictionary structure:</p> <blockquote> <p>mitosis_properties = {</p> <p> 'slide_id': slide_id,</p> <p> 'slide_height': img_h,</p> <p> 'slide_width': img_w,</p> <p> 'wsi_mitosis_count': num_mitosis,</p> <p> 'mitosis_threshold': 0.5,</p> <p> 'hotspot_rect': {'x1': hotspot[0], 'y1': hotspot[1], 'x2': hotspot[2], 'y2': hotspot[3]},</p> <p> 'hotspot_mitosis_count': mitosis_count,</p> <p> 'hotspot_mitosis_score': mitosis_score,</p> <p>}</p> </blockquote> <p> </p> <p>Proxy figure (mimicker) property dictionary structure:</p> <blockquote> <p>mimicker_properties = {</p> <p> 'slide_id': slide_id,</p> <p> 'slide_height': img_h,</p> <p> 'slide_width': img_w,</p> <p> 'wsi_mimicker_count': num_mimicker,</p> <p> 'mitosis_threshold': 0.5,</p> <p>}</p> </blockquote> <h2><br>Disclaimer:</h2> <p>It should be noted that we did not conduct a comprehensive review of all mitotic figures within each WSI, and we do not purport these to be free of errors. Nonetheless, a pathologist examined the resultant hotspot regions of interest from 757 WSIs within the TCGA-BRCA Mitosis Dataset where we found strong correlations between pathologist and MDFS mitotic counts (r=0.8, p$<$0.001). Furthermore, MDFS-derived mitosis scores are shown to be as prognostic as pathologist-assigned mitosis scores [1]. This examination was also aimed at verifying the quality of the selections, ensuring excessive false detections or artifacts did not primarily drive them and were in a plausible location in the tumor landscape.</p> <p> </p> <p>[1] Ibrahim, Asmaa, et al. "Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application." <em>Modern Pathology</em> 37.3 (2024): 100416.</p>
Data associated to the paper "Phase diagram detection via Gaussian fitting of number probability distribution"
<p>We investigate the number probability density function that characterizes subportions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.<br><br>The dataset contains the entanglement spectra of several configurations of the extended Bose-Hubbard model ground state for different systems' sizes. </p>
Transgenerational exposure to deoxygenation and warming disrupts mate detection in Gammarus locusta
<p class="s4"><span class="s9">Ocean deoxygenation and warming have been shown to pose a growing threat to the health of marine organisms and ecosystems. Yet, the potential for acclimation and adaptation remains poorly understood. The aim of this study was to evaluate the effects of transgenerational exposure to reduced oxygen availability and elevated seawater temperature on the chemosensory-dependent mating mechanisms of male amphipods </span><span class="s10"><em>Gammarus</em> <em>locusta</em></span><span class="s9">. Three subsequent generations were exposed to four experimental treatments for 30 days: i) present-day scenario, ii) warming; iii) deoxygenation; and iv) warming+deoxygenation. After exposure, the number of individuals that reached adulthood was gauged, and adult males from F<sub>0</sub> and F<sub>1</sub> were subjected to behavioral trials to assess their capacity of long-distance female cue detection through quantification of response time, first direction of movement, activity rate, and proportion of time spent in female scent cues. Ocean-warming-induced mortality and reduced oxygen availability had adverse effects on each of the investigated behavioral traits, which were amplified when combined with elevated temperature. Still, when compared to F<sub>0</sub>, the F<sub>1</sub> generation demonstrated more adaptability (i.e., higher activity rate and preference for female odors) to the combination of the two stressors, suggesting positive carry-over effects. Nevertheless, full recovery to control levels was not observed. Altogether, this study indicates that future scenarios of ocean deoxygenation and warming have the potential to disrupt chemosensory-dependent mate detection in amphipods, but also suggests possible behavioral adaptations. We call for greater research efforts on long-term impacts of ocean change on the behavioral and physiological processes of benthic coastal communities.</span></p>
Phylogenetic analysis, morphological studies, element profiling, and muscarine detection reveal a new toxic Inosperma (Inocybaceae, Agaricales) species from tropical China
Open the record for dataset details and reuse information.
Dataset from : Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser
<p>This dataset pertains to the publication 'Detection and Analysis of an Alternate Flow Pattern in a Radial Vaned Diffuser', V. Moënne-Loccoz, I. Trébinjac, N. Poujol, P. Duquesne. International Journal of Turbomachinery, Propulsion and Power. MDPI (2020).</p><p>Where possible, each figure is provided in CSV format.</p>
Figure 1 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 1. Different amount of starting material – A. The whole individual of partially engorged tick; B. Anterior half of fully-engorged tick (above red line); C. Paired DNA extraction – mouthparts (1) and a part of scutum (2).
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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