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5,509 results for “diagnosis”
Leaf spectroscopy and active fluorescence datasets for early drought and nitrogen stress diagnosis in tomato
<p>The dataset contains different plant physiological parameters collected during a 14-day stress and recovery experiment on tomato (<em>Solanum lycopersicum</em> L. cv Moneymaker) plants, undergoing a nitrogen deficiency, drought or control treatment. </p> <p>A full description of the experiment, together with the scientific results, is published by Pescador-Dionisio et al. (2024), and can be found through: <a href="https://doi.org/10.1111/nph.20253">https://doi.org/10.1111/nph.20253.</a></p> <p>The goal of the dataset collection was to obtain a non-invasive proximal sensing dataset at leaf level (reflectance, transmittance, upward and downward fluorescence), in parallel to gas exchange and active fluorescence measurements. The leaf spectroscopy dataset was further processed by a pigment spectral unmixing algorithm according to Van Wittenberghe et al. (2024), to calculate fluorescence quantum efficiency (<em><strong>FQE</strong></em>) and effective absorbance (<strong><em>A_eff</em></strong>) changes associated to the activation of regulated heat dissipation (<strong><em>A_eff_535_Xan</em></strong>). The latter absorption feature is linked to the xanthophyll ('<strong>Xan</strong>') absorption in the 500-600 nm range, which is modelled by the sum of three Gaussians. For a full description of this feature, see Van Wittenberghe et al. (2021).</p> <p>Gas exchange and active fluorescence measurements were carried out with a LI-6400 portable photosysthesis system (LI-COR Biosciences, Lincoln, USA) equipped with a 6400-40 leaf chamber fluorometer. Steady-state measurements were done at 300 and 1000 μmol m−2 s−1 ('<strong><em>PAR300</em></strong>' and '<em><strong>PAR1000</strong></em>'), i.e. growing light conditions and light saturating conditions. Light response curves were taken on different days. Common fluorescence parameters (e.g., <em><strong>Fv/Fm, Fo, Fm, NPQ, YNO, YNPQ</strong></em>) are provided together with 'sustained' and reversible' NPQ parameters calculated according Porcar-Castell (2011).</p> <p>Leaf spectroscopy and active steady-state fluorescence measurements were performed on the same measuring days ('<em><strong>d0</strong></em>', '<em><strong>d2</strong></em>', '<em><strong>d4</strong></em>', '<em><strong>d7</strong></em>', '<em><strong>d14</strong></em>') and on the same leaf, both at 300 and 1000 μmol m−2 s−1 ('<em><strong>PAR300</strong></em>' and '<em><strong>PAR1000</strong></em>'), taking into account an adaptation time. We used a LED light source and several filters, placed in front of a FluoWat leaf clip, which was connected to two high-performance VIS-NIR spectroradiometers (QEPRO, Ocean Insight Inc., Orlando, Florida, USA). The spectroscopy measurements are presented in the Matlab structures for each measuring day, e.g. "<strong><em>2023_d0_Leaf_Spec_Tomato_Stress.mat</em></strong>".</p> <p>The outputs of the pigment spectral fitting code are presented by Matlab structures, e.g. "<strong><em>2023_d0_Leaf_Fitting_Tomato_Stress.mat</em></strong>", which contains the effective absorbance fitting (<strong><em>A_eff</em></strong>) of each pigment (<strong>Chl a, Chl b, Carotene-b, Anthocyanins, and Xanthophylls</strong>) for the wavelength range [500-780] nm, the absorbed photosynthetically active radiation by Chlorophyll a ('<em><strong>APAR_Chla</strong></em>') for the wavelength range [400-800] nm, and the fluorescence quantum efficiency, calculated as the ratio of the emitted fluorescence photons and the flux of photons absorbed by Chlorophyll a. </p> <p>Additional metadata from HPLC photosynthetic pigment analyses, xanthophyll-related enzyme expression, biomass and total content of elemental nitrogen are provided.</p> <p>Please follow the README files for more detailed information.</p> <p> </p>
HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor
<p><strong>ABSTRACT</strong></p> <p>The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.</p> <p><strong>BACKGROUND</strong></p> <p>Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.</p> <p><strong>EXPERIMENTS</strong></p> <p>In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.</p> <p>The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].</p> <p>Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.</p> <p><strong>DATA DESCRIPTION</strong></p> <p>The structure of the dataset is as follows. One folder, called <em>HRV_anonymized_data</em> contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name <em>treatment_X.csv</em>, where <em>X</em> is the number of the person, while files related to the healthy controls are named <em>control_Y.csv</em>, where <em>Y</em> is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is <em>raw_RR_{control,treatment}_N.png</em>, where <em>N</em> is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>RR-interval [ms]</strong></td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds. <br> The second folder, called <em>accelerometer_anonymized_data</em> contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: <em>treatment_X.csv </em>and <em>control_X.csv</em> represent the data coming from the persons from the treatment and control group, respectively, while <em>X </em>is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>X [mg]</strong></td> <td><strong>Y [mg]</strong></td> <td><strong>Z [mg]</strong></td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.</p> <p>We also attached a file with the PANSS test scores (<em>PANSS.csv</em>) for all patients participating in the measurement. The structure of this file is as follows:</p> <table> <tbody> <tr> <td><strong>no_of_person</strong></td> <td><strong>PANSS_P</strong></td> <td><strong>PANSS_N</strong></td> <td><strong>PANSS_G</strong></td> <td><strong>PANSS_total</strong></td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> <p><br> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.</p> <p><strong>USAGE NOTES</strong></p> <p>All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the <em>main.py</em> file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the <em>utils_advanced_plots.py</em> file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting <em>sensitivity_analysis = False</em>), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting <em>exclude_quetiapine = True</em> because this medicine can have a strong impact on HRV [Hattori et al].</p> <p>The accelerometer data processing may be performed using the <em>utils_accelerometer.py</em> file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the <em>utils_loading.py </em>file.</p> <p>The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the <em>utils_basic_plots.py</em> file.</p>
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
<p>This repository includes source data used to genrtate all tables and figures for published stduy "Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer". Besides, a small set of digital images for different class of cervical smear samples are included.</p>
Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data
<p>This repository contains the dataset used in the paper "Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data" published in <strong>Scientific Reports</strong>. Please check our <a href="https://www.nature.com/articles/s41598-022-15342-z">formal publication</a> for the full details. The dataset contains 1232 nodules from 724 patients. Each row represents one nodule and each column represents one variable that describes the characteristics of the patient or nodule. The meaning of each variable is summarized below.</p> <ul> <li>id: the unique identity of the patient who carries the nodule</li> <li>age: the age of the patient</li> <li>FT3: triiodothyronine test result</li> <li>FT4: thyroxine test result</li> <li>TSH: thyroid-stimulating hormone test result</li> <li>TPO: thyroid peroxidase antibody test result</li> <li>TGAb: thyroglobulin antibodies test result</li> <li>site: the nodule location, 0: right, 1: left, 2: isthmus</li> <li>echo_pattern: thyroid echogenicity, 0: even, 1: uneven</li> <li>multifocality: if multiple nodules exist in one location, 0: no, 1: yes</li> <li>size: the nodule size in cm</li> <li>shape: the nodule shape, 0: regular, 1: irregular</li> <li>margin: the clarity of nodule margin, 0: clear; 1: unclear</li> <li>calcification: the nodule calcification, 0: absent, 1: present</li> <li>echo_strength: the nodule echogenicity, 0: none, 1: isoechoic, 2: medium-echogenic, 3: hyperechogenic, 4: hypoechogenic</li> <li>blood_flow: the nodule blood flow, 0: normal, 1: enriched</li> <li>composition: the nodule composition, 0: cystic, 1: mixed, 2: solid</li> <li>multilateral: if nodules occur in more than one location, 0: no, 1: yes</li> <li>mal: the nodule malignancy, 0: benign, 1: malignant</li> </ul>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p><span>Summary: </span></p> <p><span><span> </span>Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</span></p> <p><span><span> </span>The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</span></p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p>Summary:</p> <p> Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</p> <p> The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</p>
Improving Automatic Melanoma Diagnosis using Deep Learn-ing-based Segmentation of Irregular Networks
<p>Irregular masks dataset created on a subset of the ISIC19 training dataset. All annotations are for melanoma lesions. The filename indicates the ISIC19 image id along with suffix indicating annotator and/or verifier. This dataset was used in the publication "Improving Automatic Melanoma Diagnosis using Deep Learn-ing-based Segmentation of Irregular Networks" to be submitted to the Cancers Journal.</p> <p>Please cite the corresponding article (to be published) if data is used in your work.</p> <p>The references for the ISIC19 dataset that this is built on is given below.</p> <blockquote> <p>BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona</p> <p>HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; <a href="https://doi.org/10.1038/sdata.2018.161">https://doi.org/10.1038/sdata.2018.161</a></p> <p>MSK Dataset: (c) Anonymous; <a href="https://arxiv.org/abs/1710.05006">https://arxiv.org/abs/1710.05006</a>; <a href="https://arxiv.org/abs/1902.03368">https://arxiv.org/abs/1902.03368</a></p> </blockquote>
Dataset:Biomarker-based diagnosis of Post COVID-19 Condition
<p>The persistence or development of new symptoms three months after the initial acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection is referred to as post-coronavirus disease (COVID) condition (PCC). The identification of new biomarkers specific for the occurrence of PCC is vital to proceed in the future towards prediction of its evolution.</p> <p>This study was registered with ISRCTN Registry during recruitment (ISRCTN27312680), and it was conducted comparing two parallel groups: individuals diagnosed with PCC versus individuals who completely recovered within 3 months after acute COVID-19.</p> <p>All participants were enrolled between the first semester of 2022 in Primary Health Care Centers (PHCCs) of Zaragoza (Spain). The two parallel groups were matched by age, gender, and date of acute COVID-19 diagnosis. The diagnosis of PCC was determined by a general practitioner, following the WHO criteria [7], before or at the time of inclusion in the study. Recovered individuals were required to have passed acute COVID-19, confirmed by RT-qPCR, antigen test, or SARS-CoV-2 serology.<strong> </strong></p>
FIG. 3 in Squalus rancureli Fourmanoir, 1979, a new junior synonym of the blacktailed spurdog S. melanurus Fourmanoir, 1979, and updated diagnosis of S. bucephalus Last, Séret & Pogonoski, 2007 from New Caledonia (Squaliformes, Squalidae)
FIG. 3. — First and second dorsal fins of Squalus melanurus Fourmanoir, 1979: A, B, MNHN-IC-1997-3619, juvenile female, 527 mm TL; C, D, MNHN-IC-2002-1196, adult male, 655 mm TL; E, F, MNHN-IC-1978-0693 (holotype of S. rancureli Fourmanoir, 1979), adult male, 680 mm TL. Scale bars: 20 mm.
FIG. 8 in Squalus rancureli Fourmanoir, 1979, a new junior synonym of the blacktailed spurdog S. melanurus Fourmanoir, 1979, and updated diagnosis of S. bucephalus Last, Séret & Pogonoski, 2007 from New Caledonia (Squaliformes, Squalidae)
FIG. 8. — Scanning electron microscopy of dermal denticles of Squalus melanurus Fourmanoir, 1979, MNHN-IC-1997-3627, adult female, 690 mm TL. Scale bars: A, 100 μm; B, 200 μm.
FIG. 5. — A, B in Squalus rancureli Fourmanoir, 1979, a new junior synonym of the blacktailed spurdog S. melanurus Fourmanoir, 1979, and updated diagnosis of S. bucephalus Last, Séret & Pogonoski, 2007 from New Caledonia (Squaliformes, Squalidae)
FIG. 5. — A, B, Caudal fin of Squalus melanurus Fourmanoir, 1979, showing black lower caudal lobe: A, MNHN-IC-1997-3619, juvenile female, 527 mm TL; B, MNHN-IC-2002-1197, adult male, 655 mm TL; C, caudal fin of holotype of S. rancureli Fourmanoir, 1979 (MNHN-IC-1978-0693). Scale bars: 20 mm.
Fig. 2 in Revised Diagnosis of the Rare Clingfish Kopua nuimata (Gobiesocidae) with Notes on Fresh Coloration and First Australian Record
Fig. 2. Kopua nuimata (CSIRO H6007-17, 29.0 mm SL), Norfolk Ridge, south of Norfolk Island, Australia. Photographed subsequent to thawing. Photographs provided by CSIRO.
Fig. 16 in Contributions to the knowledge of Formicidae (Hymenoptera, Aculeata): a new diagnosis of the family, the first global male-based key to subfamilies, and a treatment of early branching lineages
Fig. 16. Male representatives of three subfamilies. A–B. Formica wheeleri, Formicinae (U.S.A., CASENT0173024, A. Nobile). C–D. Rhytidoponera, Ectatomminae, ectaheteromorph clade (Australia, CASENT0004610, A. Nobile). E–F. Pogonomyrmex rastratus (Argentina, CASENT0172673, A. Nobile). Scale bars: A, C = 0.5 mm, B, D, F = 1.0 mm, E = 0.2 mm.
Fig. 9 in Contributions to the knowledge of Formicidae (Hymenoptera, Aculeata): a new diagnosis of the family, the first global male-based key to subfamilies, and a treatment of early branching lineages
Fig. 9. Apomyrma CD01, male, photomicrographs. A. Forewing. B. Hindwing. C. Abdominal sternum IX, ventral view. D. Genital capsule, dorsal view. E. Genital valves, slightly splayed and without cupula, ventral view. F. Genital capsule, lateral view. G. Volsella and paramere, mesal view. H. Penisvalva in situ, mesal view. Scale bars: A–B = 0.5 mm, C–H = 0.1 mm. Abbreviations: see Material and Methods.
Fig. 10 in Contributions to the knowledge of Formicidae (Hymenoptera, Aculeata): a new diagnosis of the family, the first global male-based key to subfamilies, and a treatment of early branching lineages
Fig. 10. Representative males of Leptanillinae, lateral view A. Protanilla "TH01" (Thailand, CASENT0119776, A. Nobile), arrow indicates loss of abdominal segment II petiolation. B. Protanilla "TH03" (Thailand, CASENT0119791, E. Prado). C. Leptanilla swani (Australia, CASENT0172318, A. Nobile). D. Protanilla sp. (Indonesia, CASENT0178838, A. Nobile), arrow indicates basolateral basimeral process. E. Scyphodon sp. (Indonesia, MCZ155112w, A. Nobile). F. Noonilla sp., used with permission from Petersen (1968). Scale bars: A, C, E–F = 0.2 mm, D = 0.5 mm, B = 1.0 mm.
Fig. 12 in Contributions to the knowledge of Formicidae (Hymenoptera, Aculeata): a new diagnosis of the family, the first global male-based key to subfamilies, and a treatment of early branching lineages
Fig. 12. Martialis heureka Rabeling & Verhaagh, 2008, male, wing photomicrographs and genitalia illustrations, genital membranes not shown. A. Forewing. B. Hindwing. C. Abdominal sternum IX, ventral view. D. Genital capsule, dorsal view. E. Genital capsule, ventral view. F. Genital capsule, lateral view. G. Volsella and paramere, mesal view. H. Penisvalva in situ, mesal view. Scale bars: A–B = 0.5 mm, C–H = 0.1 mm. Abbreviations: see Material and Methods.
Fig. 15 in Contributions to the knowledge of Formicidae (Hymenoptera, Aculeata): a new diagnosis of the family, the first global male-based key to subfamilies, and a treatment of early branching lineages
Fig. 15. Male representatives of three subfamilies. A, D. Frontal view. B–C, E. Lateral view. — A–B. Pseudomyrmex holmgreni, Pseudomyrmecinae (Paraguay, CASENT0173758, A. Nobile). C. Aneuretus simoni, Aneuretinae, used with permission from Wilson et al. (1956). D–E. Technomyrmex difficilis, Dolichoderinae (Madagascar, CASENT0049968, A. Nobile). Scale bars: A, D = 0.2 mm, B = 1.0 mm, E = 0.5 mm, no scale available for C.
ScienceDex guides
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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.