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68 results for “CTX”
CTX DTM and ORI Mosaics over Sakarya Vallis, Gale Crater, Mars
<p>Local digital terrain model (DTM) and orthorectified image (ORI) mosaics over Sakarya Vallis, west of Aeolis Mons in Gale crater, Mars. The two constituent DTMs were processed using the CASP-GO suite described in Tao et al. (2018); the ORIs were processed using Ames Stereo Pipeline. The DTMs were then co-registered to an HRSC DTM mosaic (Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808354) and each other using Ames Stereo Pipeline, and then cropped and mosaicked.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> DTM grid-spacing: 18 m/pixel<br> ORI resolution: 6 m/pixel</p> <p>Stereo pairs (from Grindrod and Davis, 2018):</p> <ul> <li>P04_002675_1746_XI_05S222W, B21_017786_1746_XN_05S222W</li> <li>D02_027834_1748_XN_05S222W, G04_019698_1747_XI_05S222W</li> </ul> <p>Image IDs of the ORIs: P04_002675_1746_XI_05S222W, D02_027834_1748_XN_05S222W</p>
Mars orbital images of fresh impacts from CTX
<p><strong>Mars orbital images of fresh impacts from CTX</strong></p> <p>This data set contains images obtained from observations of Mars by the Context Camera (CTX) on the Mars Reconnaissance Orbiter. Each image in this collection is a cropped region of roughly 1.8 x 1.8 km (300 x 300 pixels at 6 m/pixel). The data set consists of "positive" images centered on the location of known fresh impacts on the surface of Mars (using a fresh impact catalog [1]) and "negative" examples obtained by randomly sampling CTX images (uniformly over the surface of Mars).</p> <p><strong>Contents</strong></p> <p>The 6829 images are divided into training and validation sets to capture the configuration used to train a fresh impacts image classifier [2]. However, they can be pooled together as a single data set for other purposes (but note that augmented versions are provided only for the training data). </p> <ul> <li>training_data/: 6156 images + 5 augmented versions per image</li> <li>validation_data/: 673 images (not augmented)</li> </ul> <p>Labels are indicated in the filenames. Positive examples start with a 0, and negative examples start with a - (negative sign).</p> <p>We employed augmentation on the training examples to generate 5 augmented versions per original image. The augmentation is indicated at the end of the filename: </p> <ul> <li>AUG-HF: Horizontal flip</li> <li>AUG-VF: Vertical flip</li> <li>AUG-RO: Rotate (randomly choose 90, 180, or 270 degrees)</li> <li>AUG-CJ: Image adjustment (minor random adjustment to brightness/contrast/saturation/hue)</li> <li>AUG-BL: Blur (Gaussian filter with radius 2) </li> </ul> <p><strong>References</strong> </p> <p>[1] Daubar, I.J., McEwen, A.S., Byrne, S., Kennedy, M.R., and Ivanov, B. (2013). "The current martian cratering rate," Icarus 225, 506-516. doi:10.1016/j.icarus.2013.04.009.</p> <p>[2] Munje, M. (2021). Martian Fresh Impact Classifier (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.552336</p> <p> </p>
Study to Compare a Mono Atezolizumab Window Followed by a Atezolizumab - CTX Therapy With Atezolizumab - CTX Therapy
ClinicalTrials.gov study NCT04770272. IPD Sharing: NO. Countries: 1. Publications: 42.
Measurement of biochemical Marker Dynamics in serum levels of calcium, phosphorus, P1NP, and CTX across three time points (T0, T1, T2) following treatment with dimeric R25CPTH(1-34), rhPTH(1-34), and control
Open the record for dataset details and reuse information.
Topographic data for the study "Scoria cones on Mars: detailed investigation of morphometry based on HiRISE and CTX DEMs"
<p>These are topographic data based on gridded digital elevation models (DEMs) derived from HiRISE (~30 cm/pixel, [McEwen et al., 2007]) and CTX (5–6 m/pixel; [Malin et al., 2007]) images which were used for morphometric investigation of cones within three fields on Mars (Ulysses Collse, Hydraotes Colles and unnamed field in Coprates Chasma).</p>
Mycophenolate Mofetil (MMF) Versus Intravenous CTX Pulses in the Treatment of Adult Severe HSPN
ClinicalTrials.gov study NCT00301613. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of CTX-712 in Relapsed/Refractory Acute Myeloid Leukemia and Higher Risk Myelodysplastic Syndromes
ClinicalTrials.gov study NCT05732103. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study of CTX-009 in Combination With Paclitaxel in Adult Patients With Unresectable Advanced, Metastatic or Recurrent Biliary Tract Cancers (COMPANION-002)
ClinicalTrials.gov study NCT05506943. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Phase II Study With Catumaxomab in Patients With Gastric Cancer After Neoadjuvant CTx and Curative Resection
ClinicalTrials.gov study NCT00464893. IPD Sharing: Not stated. Countries: 4. Publications: 5.
Safety Trial Of CTX Cells In Patients With Lower Limb Ischaemia
ClinicalTrials.gov study NCT01916369. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Prevalence of CTX Disorder in Juvenile Cataract Cases in Turkey
ClinicalTrials.gov study NCT03584893. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Study of CTX-471 as a Monotherapy or in Combination With Pembrolizumab in Patients Post PD-1/PD-L1 Inhibitors in Metastatic or Locally Advanced Malignancies
ClinicalTrials.gov study NCT03881488. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CTX-UXO: A Comprehensive Dataset for Detection and Identification of UneXploded Ordnances
<p><span lang="EN-US">According to US NOAA, unexploded ordnances (UXO) are “explosive weapons such as bombs, bullets, shells, grenades, mines, etc. that did not explode when they were employed and still pose a risk of detonation”. UXOs are among the most dangerous threats to human life, environment and wildlife protection as well as to economic development. The risks associated with UXOs do not discriminate based on age, gender, or occupation, posing a danger to anyone unfortunate enough to encounter them. Contrary to expectations, an UXO is more hazardous than new ordnance, as its arming or initiation mechanisms may be active or compromised. A mistake in correctly identifying the ordnance type can be fatal, which is why a decision support system can assist in making decisions under continuous stress, where lives are at risk. Recent advances in image processing techniques and deep learning demonstrate that object detection and identification can be applied across multiple domains. However, until now, UXO detection has been limited by the lack of a representative, comprehensive dataset that provides robustness across different scenarios. UXOs are often found in altered, oxidized, semi-buried states in hard-to-reach environments.</span></p> <p><span lang="EN-US">We thus propose the <strong>Contextual Vision for Unexploded Ordnances (CTX-UXO) </strong>dataset, which provides a collection of <strong>labeled UXO images</strong> in various visual contexts within the visible spectrum. The dataset encompasses ammunitions in different stages, across multiple environments, angles, distances, and with various types of cameras. Additionally, replicas of munitions, faithfully replicating the characteristics of real ordnance, were used to diversify the dataset by relocating or arranging them in new positions and environments, and by removing certain ordnance components. This approach aims to create a dataset that is as varied and representative of real-world scenarios as possible. The dataset will be periodically updated with new types of UXO in different visual contexts.</span></p> <p><span lang="EN-US">We hope that this dataset represents a useful resource for researchers and engineers working on supervised and semi-supervised object recognition projects, with particular emphasis on civil protection and emergency situation management applications.</span></p> <p><span lang="EN-US">The CTX-UXO dataset has been rigorously validated using a wide range of deep learning architectures and methodological frameworks, incorporating diverse preprocessing techniques. The resulting findings have been disseminated through peer-reviewed scientific publications:</span></p> <p><span lang="EN-US">[1] Craioveanu M., Stamatescu G., Popescu D., Ensemble Strategy with Multi-Step Hard Sample Mining for Improved UXO Localisation and Classification", IEEE Access, vol. 13, pp. 123546-123558, 2025. </span></p> <p><span lang="EN-US">[2] Craioveanu M., Stamatescu G., Detection and Identification of Unexploded Ordnance using a Two-Step Deep Learning Methodology, 32nd Mediterranean Conference on Control and Automation, MED 2024, June 11-14, Chania, Greece.</span></p> <p><span lang="EN-US">[3] Craioveanu M., Stamatescu G., Evaluation of the Robustness-Runtime Efficiency Trade-Off of Edge AI Models in UXO Localisation and Classification, 33rd Mediterranean Conference on Control and Automation, MED 2025, June 10-13, Tangier, Morocco.</span></p> <p><span lang="EN-US">We would like to thank the personnel of the </span><span lang="EN-US"><a href="https://igsu.ro/">National Romanian Inspectorate for Emergency Situations</a></span><span lang="EN-US"> for their logistical support in the collection and dissemination of this dataset.</span></p> <h4><span lang="EN-US">Instructions:</span></h4> <p><span lang="EN-US">The dataset includes overall 15 449 instances in 3520 images, 4.38 instances/images, images grouped into three folders (train, validation, test). The images are formated as jpg files with a median image ratio 2124 px square and RGB color space. The images were captured using various devices, predominantly mobile phones with high-performance cameras. The most frequently used camera was a 64MP model equipped with the Samsung GW3 sensor (S5KGW3). This sensor is a 1/1.97" imager with 0.7µm pixels and employs Tetra-cell technology to enhance image quality. It is paired with a 25mm f/1.8 lens. </span></p> <p><span lang="EN-US">Both YOLO and COCO annotation formats are supported. The dataset is structured into multiple repositories, each adapted for specific computer vision tasks such as binary classification, multi-class detection or instance segmentation. Future releases will include additional UXO types captured under varied environmental and lighting conditions.</span></p> <p><span lang="EN-US">The first repository includes unannotated images, divided into the same three subsets, with the data split performed using the Multilabel Stratified Shuffle Split algorithm to maintain class balance. The distribution follows a 70% training portion, with 15% allocated to both validation and test sets.</span></p> <p><span lang="EN-US">For binary detection and classification, 15 449 instances can be utilized (UXO/NON-UXO). Moreover, classification into specific classes (Mortar Bomb, Projectile, Grenade, Aviation Bomb, RPG, LandMine, Rockets, AntiSubmarine, Cartridge, Fuse) is feasible by labeling the data accordingly: 6 121 instances of projectiles, 4 269 instances of mortar bombs, 3 399 instances of grenades, 987 Cartridge, 333 Aviation Bombs, 124 Cartridge Magazine, 92 Fuses, 63 rocket-propelled grenades, 29 Landmines, 21 rockets, 6 antisubmarine, 5 sea mine. Instances can appear individually or within the same images, ensuring high diversity.</span></p>
Phase 3 Efficacy and Safety Study in Adults With ADHD Using CTx-1301.
ClinicalTrials.gov study NCT05631626. IPD Sharing: Not stated. Countries: 1. Publications: 0.
EMPIRE CF: A Phase 2 Study to Evaluate the Efficacy, Safety, and Tolerability of CTX-4430 in Adult Cystic Fibrosis (CF) Patients
ClinicalTrials.gov study NCT02443688. IPD Sharing: Not stated. Countries: 7. Publications: 0.
Cerebrotendinous Xanthomatosis (CTX) Prevalence Study
ClinicalTrials.gov study NCT02638220. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CTx-1301 Comparative Bioavailability Study
ClinicalTrials.gov study NCT04138498. IPD Sharing: NO. Countries: 1. Publications: 0.
Phase 3 Efficacy and Safety Fixed-Dose Study in Pediatrics (6-17) With ADHD Using CTx-1301
ClinicalTrials.gov study NCT05286762. IPD Sharing: NO. Countries: 1. Publications: 0.
ChIP-seq and RNA-Seq analyses of epithelial and mesenchymal cells - HMLE, N8, N8-CTx
GEO Series GSE74883. Homo sapiens. 29 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Gene expression profile at single cell level of mouse tibialis anterior muscle cells from young and aged mice treated with or without cardiotoxin (CTX)
GEO Series GSE272412. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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