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Fig. 13 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 13. Projection of individual scores in the space of first and second Principal Component axis for the populations of females of Astyanax laticeps.
Fig. 12 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 12. Projection of individual scores in the space of first and third Principal Component axis for the populations of males and females of Astyanax laticeps.
Fig. 10 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 10. Tukey box plots of number of scales around the caudal peduncle in Astyanax laticeps populations by river drainage from south to north. Mean represented by thick vertical bar and 25th and 75th percetiles as lateral borders of box plots.
Fig. 9 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 9. Tukey box plots of number of perforated scales of lateral line in Astyanax laticeps populations by river drainage from south to north. Mean represented by thick vertical bar and 25th and 75th percetiles as lateral borders of box plots.
Fig. 8. Astyanax laticeps, UFRGS 4576 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 8. Astyanax laticeps, UFRGS 4576, male, 49.2 mm SL. SEM image of upper and lower jaws, right side. Scale bar = 1 mm.
Fig. 7. Astyanax laticeps, MCP 35425, 61.2 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 7. Astyanax laticeps, MCP 35425, 61.2 mm SL, tributary of rio Ivaí, rio Jacuí drainage, Júlio de Castilhos, Rio Grande do Sul, Brazil.
Fig. 5 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 5. Map of southern Brazil and Uruguay, showing the distribution of examined material of Astyanax obscurus (circles), and Astyanax laticeps (squares). Some symbols represent more than one lot or locality.
Fig. 3. Astyanax obscurus, MCP 26125, 65.5 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 3. Astyanax obscurus, MCP 26125, 65.5 mm SL. SEM image of upper and lower jaws, right side. Scale bar = 2 mm.
Fig. 4 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 4. Lateral view of left side of anterior region showing humeral spots of (a) Astyanax obscurus, MCP 26125, 75.6 mm SL, and (b) Astyanax laticeps, MCP 26127, 71.1 mm SL.
Fig. 2. Astyanax obscurus, MCP 40000, 59.2 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 2. Astyanax obscurus, MCP 40000, 59.2 mm SL, rio Cadeia above the large waterfalls, Santa Maria do Herval, Rio Grande do Sul, Brazil.
Fig. 1. Astyanax obscurus, ZMB 7478 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 1. Astyanax obscurus, ZMB 7478, syntype, 57.8 mm SL, rio Cadeia above of the large waterfalls, Santa Maria do Herval, Rio Grande do Sul, Brazil.
Fig. 11 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 11. Tukey box plots of number of branched anal-fin rays in Astyanax laticeps populations by river drainages from south to north. Mean represented by thick vertical bar, and 25th and 75th percetiles as lateral borders of box plots.
Fig. 14 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 14. Projection of individual scores in the space of first and second Principal Component axis for the populations of males of Astyanax laticeps.
Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a
Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).
Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation
<p><strong>Background</strong>: Prostate-related quality of life can be assessed with a variety of different questionnaires. The 50-item Expanded Prostate Cancer Index Composite (EPIC) and the International Prostate Symptom Score (IPSS) are two widely used options. The goal of this study was, therefore, to develop and validate a model that is able to convert between the EPIC and the IPSS to enable comparisons across different studies. </p> <p><strong>Methods</strong>: Three hundred forty-seven consecutive patients who had previously received radiotherapy and surgery for prostate cancer at two institutions in Switzerland and Germany were contacted via mail and instructed to complete both questionnaires. The Swiss cohort was used to train and internally validate different machine learning models using fourfold cross-validation. The German cohort was used for external validation.</p> <p><strong>Results</strong>: Converting between the EPIC Urinary Irritative/Obstructive subscale and the IPSS using linear regressions resulted in mean absolute errors (MAEs) of 3.88 and 6.12, which is below the respective previously published minimal important differences (MIDs) of 5.2 and 10 points. Converting between the EPIC Urinary Summary and the IPSS was less accurate with MAEs of 5.13 and 10.45, similar to the MIDs. More complex model architectures did not result in improved performance in this study. The study was limited to the German versions of the respective questionnaires.</p> <p><strong>Conclusions</strong>: Linear regressions can be used to convert between the IPSS and the EPIC Urinary subscales. While the equations obtained in this study can be used to compare results across clinical trials, they should not be used to inform clinical decision-making in individual patients. Trial registration This study was retrospectively registered on clinicaltrials.gov on January 14th, 2022, under the registration number NCT05192876.</p>
Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2024 - Validation Dataset
<blockquote> <p><strong>This Challenge will be an open-ended challenge, and we welcome your submission. Please register your team via this <a title="https://forms.office.com/e/dPg47ktV7M" href="https://forms.office.com/e/dPg47ktV7M" target="_blank" rel="noopener">form</a>. You can submit the algorithm via this <a title="https://forms.office.com/e/QChhNkLYiu" href="https://forms.office.com/e/QChhNkLYiu" target="_blank" rel="noopener noreferrer">form</a> for TUS-REC2024 Challenge, and we will test your submitted docker on the test set.</strong></p> <p><strong>We are organising TUS-REC2025 at MICCAI2025. More information is available on the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC2025 challenge website</a> and <a href="https://github.com/QiLi111/TUS-REC2025-Challenge_baseline" target="_blank" rel="noopener">Baseline code repo</a>.</strong></p> </blockquote> <p><strong>This is the validation dataset. The training dataset is available at <a href="../doi/10.5281/zenodo.11178508" target="_blank" rel="noopener">Part1</a>, <a href="../doi/10.5281/zenodo.11180795" target="_blank" rel="noopener">Part2</a>, and <a href="../doi/10.5281/zenodo.11355499" target="_blank" rel="noopener">Part3</a>.</strong></p> <p>Acquisition devices and config: The 2D US images were acquired using an Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40). The associated position information of each frame was recorded by an optical tracker (NDI Polaris Vicra, Northern Digital Inc., Canada). The acquired US frames were recorded at 20 fps, with an image size of 480×640, without speckle reduction. The frequency was set at 6MHz with a dynamic range of 83 dB, an overall gain of 48% and a depth of 9 cm. </p> <div> <p>Scanning protocol: Both left and right forearms of volunteers were scanned. For each forearm, the US probe moves in three different trajectories (straight line shape, "C" shape, and "S" shape), in a distal-to-proximal direction followed by a proximal-to-distal direction, with the US plane perpendicular of and parallel to the scanning direction. The validation dataset contains 72 scans in total, 24 scans associated with each subject.</p> <p>For detailed information please refer to the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/TUS-REC2024/" target="_blank" rel="noopener">Challenge website</a>. Baseline code is also provided, which can be found at this <a href="https://github.com/QiLi111/tus-rec-challenge_baseline" target="_blank" rel="noopener">repo</a>.</p> <p>Dataset structure: </p> <ul> <li>Folder <code>frames</code>: contains three folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing image of each frame within this scan. Key-value pair and name of each .h5 file are explained below. <ul> <li>“frames” - All frames in the scan; with a shape of [N,H,W], where N refers to the number of frames in the scan, H and W denote the height and width of a frame. </li> <li>Notations in the name of each .h5 file: “RH”: right arm; “LH”: left arm; “Per”: perpendicular; “Par”: parallel; “L”: straight line shape; “C”: C shape; “S”: S shape; “DtP”: distal-to-proximal direction; “PtD”: proximal-to-distal direction; For example, “RH_Per_L_DtP.h5” denotes a scan on the right forearm, with ultrasound probe perpendicular of the forearm sweeping along straight line, in distal-to-proximal direction.</li> </ul> </li> </ul> </div> <div> <ul> <li>Folder <code>transfs</code>: contains three folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing transformation of each frame within this scan. Key-value pair and name of each .h5 file are explained below. <ul> <li>“tforms” - All transformations in the scan; with a shape of [N,4,4], where N is the number of frames in the scan, and the transformation matrix denotes the transformation from tracker tool space to camera space. </li> <li>Notations in the name of each .h5 file is the same as in folder <code>frames</code>.</li> </ul> </li> <li>Folder <code>landmark</code>: contains three .h5 files. Each corresponds to one subject, storing coordinates of landmarks for 24 scans of this subject. For each scan, the coordinates are stored in numpy array with a shape of [20,3]. The first column is the index of frame; the second and third columns denote the coordinates of landmarks in the image coordinate system.</li> <li><code>calib_matrix.csv</code>: The calibration matrix was obtained using a pinhead-based method. The "scaling_from_pixel_to_mm" and "spatial_calibration_from_image_coordinate_system_to_tracking_tool_coordinate_system" are provided in the “calib_matrix.csv”.</li> <li><code>dataset_keys.h5</code>: stores the paths to all the scans of the data set. Keys in “dataset_keys.h5” denotes all the available scans in validation set, in a format of “sub%03d__%s” where %03d denotes folder name, and %s denotes the scan name. For example, “sub050__LH_Par_C_DtP” means the scan in folder “050”, with file name of “LH_Par_C_DtP.h5”</li> </ul> <div> <p><strong>Data Usage Policy:</strong></p> <ul> <li>The training and validation data provided may be utilized within the research scope of this challenge and in subsequent research-related publications. However, commercial use of the training and validation data is prohibited. In cases where the intended use is ambiguous, participants accessing the data are requested to abstain from further distribution or use outside the scope of this challenge.</li> <li><span>If you use our dataset in your publication, </span>please cite the challenge paper and some of the following optional articles: <ul> <li>Challenge paper: <ul> <li><strong>Qi Li et al. "TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker." <em>arXiv preprint arXiv:<a title="https://arxiv.org/abs/2506.21765" href="https://doi.org/10.48550/arXiv.2506.21765" target="_blank" rel="noopener">2506.21765</a></em> (2025).</strong></li> </ul> </li> <li>Optional articles:<br> <ul> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14.</a></li> </ul> </li> </ul> </li> </ul> </div> </div>
Figs 1-5 in Platambus striatus (ZENG & PU 1992) a valid species from south-western China (Coleoptera: Dytiscidae)
Figs 1-5: Platambus striatus (ZENG & PU): (1) habitus, specimen from Yunnan; (2) shape of pronotum; (3) median lobe in lateral view; (4) median lobe in ventral view; (5) paramere.
Figs 37-39 in Notes on Graptodytes SEIDLITZ, 1887, re-instatement of G. laeticulus (SHARP, 1882) as valid species and description of Tassilodytes nov.gen. from Algeria (Coleoptera, Dytiscidae, Hydroporinae, Siettitiina)
Figs 37-39: Tassilodytes parisii (GRIDELLI, 1939), holotype (37) lateral view, showing almost straight elytral margin near shoulder; (38) ventral view, showing vaulted prosternum before procoxae, prosternal column (between procoxae) and prosternal process; (39) ventral view, arrows pointing on anteromedial metaventral process and small visible part of right side of "mesosternal fork" serving to receive prosternal process (see SHARP 1882: 224).
Figs 42-49 in Notes on Graptodytes SEIDLITZ, 1887, re-instatement of G. laeticulus (SHARP, 1882) as valid species and description of Tassilodytes nov.gen. from Algeria (Coleoptera, Dytiscidae, Hydroporinae, Siettitiina)
Figs 42-49: Collecting sites: (42) Chaabat Errich; (43) Aïn Chef; (44) Mouilha Zizlag; (45) Aïn Damous; (46) Chaabat Settara; (47) Chaabat Sangot; (48) Mouilha Ben Haouech; (49) El K'haïla; all localities situated in Medjez Sfa region (Guelma province).
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.