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2,048 results for “low dose”
Low dose rate radiation induced secretion of TGF-β3 together with an activator in small extracellular vesicles modifies low dose hyper-radiosensitivity through ALK1 binding
<p>This is a collection of results from all clonogenic assays on T-47D cells used in the manuscript "Low dose rate radiation induced secretion of TGF-β3 together with an activator in small extracellular vesicles modifies low dose hyper-radiosensitivity through ALK1 binding". </p> <p>T-47D cells were subjected to various pretreatments: low dose rate priming (0.1-0.3 Gy/h for 1 hour), small extracellular vesicles from irradiated or control cells, irradiated or control cell conditioned medium, MMP/ADAM inhibitor TAPI-2, recombinant TGF-B3, inhibitors of ALK1, ALK2, ALK5 or TGF-BRII, iNOS inhibitor 1400W, recombinant FKBP4, recombinant MMP14 and combinations of these. All pretreatments except low dose rate priming was administered for 24 hours. </p> <p>After pretreatments, cells were seeded to colonies and irradiated with 220 kV x-rays at a dose rate of 22.5 Gy/h or gamma rays from a Co-60 source at a dose rate of 20-25 Gy/h. </p> <p>Colonies were cultured for 2-3 weeks before fixation and manual counting. </p>
Composite of graphene and silver nanoparticles obtained by low-dose gamma irradiation
<p>Content:</p> <p>TEM.zip - TEM images of composites, file type .tif and .dm3</p> <p>VINCA_FTIR.zip - FTIR data of graphene oxide and exfoliated graphene, file type .csv</p> <p>VINCA_UV-Vis.zip - FTIR data of graphene oxide and exfoliated graphene composites, file type .csv</p> <p>IEMN_VNA_Graphene-AgNP - EMI shielding measurement of graphene oxide and exfoliated graphene composites, file type .xlsx</p> <p>VINCA_CA_graphene-AgNP.jpg - contact angle measurement of graphene oxide and exfoliated graphene composites</p> <p>VINCA_532nmLASER.zip - temperature elevation measurements of graphene oxide and exfoliated graphene composites, file type .csv</p> <p>FTPO_TGA_Graphene-AgNP.zip - TGA data of graphene oxide and exfoliated graphene composites, file type .txt</p> <p> </p> <p> </p>
Figure 2 in Efficacy of low-dose EarthTec QZ treatment for the control of New Zealand mud snails Potamopyrgus antipodarum in a hatchery environment
Figure 2. Mean percent of active individuals with standard error (SE) plotted against days of treatment for three species of snail at Page Springs Hatchery.
Maximum likelihood reconstruction of low-dose STEM data
<p>This is a test data set for the low-dose reconstruction software available at <a href="https://github.com/Brow71189/low-dose-reconstruction">github.com/Brow71189/low-dose-reconstruction</a>. It is part of my PhD thesis, in particular the appendix, where the reconstruction process is described and the reader should be able to reproduce the results with this data set and the above mentioned software.</p>
Fig. 1 in Evaluating the use of a low dose fipronil bait in reducing black-tailed prairie dog (Cynomys ludovicianus) fleas at reduced application rates
Fig. 1. Flea prevalence (0.0–1.0) during pre-treatment and post-treatment for (A) captured black-tailed prairie dogs (Cynomys ludovicianus); and (B) active burrows. Vertical bars indicate the 95% confidence intervals (95% CI).
Dose-dependent progression of multiple low dose streptozotocin-induced diabetes in mice - DEGs
<p><strong>Differentially expressed genes in mice treated with multiple low doses of streptozotocin.</strong></p> <p> All animals were maintained in accordance with City of Hope Institutional Animal Care and Use Committee protocols (#16047) in accordance with the Guide for the Care and Use of Laboratory Animals (11). Only male C57BL/6J were used due to the varying effectiveness of STZ in female mice. All mice were housed in individually ventilated cages with 3-5 mice/cage. Animals were maintained on 12-h light:dark cycles with <em>ad libitum</em> access to water and standard rodent chow diet.</p> <p> STZ (Sigma-Aldrich S0130-1G; St. Louis, MO, USA) was administered for five consecutive days by intraperitoneal (IP) injections of STZ dissolved right before the injection in saline at either 35 or 55 mg/kg body weight. Saline was administered to 34 mice as a vehicle control, 40 mice received 35 mg/kg STZ, and 38 mice received 55 mg/kg STZ. Equivalent volumes of saline were used in control animals. Approximately 75-100 healthy islets per mouse were picked by hand, washed in cold PBS, and then resuspended in 350 ul of RLT with 1%BME. RNA was isolated using RNeasy micro (Qiagen 7404; Hilden, Germany) kits according to manufacture instructions and DNA was digested by treatment with RNAse-Free DNAse Set (Qiagen 79254). </p> <p> RNA sequencing libraries were prepared using 150 ng total RNA with Kapa RNA mRNA HyperPrep kit (Kapa Biosystems, Cat KR1352; Cape Town, South Africa) to enrich for mRNA according to the manufacturer's protocol with a final PCR cycle of 12x. The RNA fragment time was 8 min at 94 C; 1.0x beads purification was used for the final sequencing library purification. The final libraries were validated with the Agilent Bioanalyzer DNA High Sensitivity Kit and quantified with Qubit. Sequencing was performed on Illumina NovaSeq 6000 by using S4 kit v1.5 with the paired end mode of 2x101cycle. Real-time analysis (RTA) v3.4.4 software was used for base calling. The number of biological replicates were as follows; saline control: 6, STZ, 35 mg/kg: 7, STZ, 55 mg/kg: 8.</p>
Data from: Safety of single low-dose primaquine in glucose-6-phosphate dehydrogenase deficient falciparum-infected African males: two open-label, randomized, safety trials
Background: Primaquine (PQ) actively clears mature Plasmodium falciparum gametocytes but in glucose-6-phosphate dehydrogenase deficient (G6PDd) individuals can cause hemolysis. We assessed the safety of low-dose PQ in combination with artemether-lumefantrine (AL) or dihydroartemisinin-piperaquine (DP) in G6PDd African males with asymptomatic P. falciparum malaria. Methods and findings: In Burkina Faso, G6PDd adult males were randomized to treatment with AL alone (n = 10) or with PQ at 0.25 (n = 20) or 0.40 mg/kg (n = 20) dosage; G6PD-normal males received AL plus 0.25 (n = 10) or 0.40 mg/kg (n = 10) PQ. In The Gambia, G6PDd adult males and boys received DP alone (n = 10) or with 0.25 mg/kg PQ (n = 20); G6PD-normal males received DP plus 0.25 (n = 10) or 0.40 mg/kg (n = 10) PQ. The primary study endpoint was change in hemoglobin concentration during the 28-day follow-up. Cytochrome P-450 isoenzyme 2D6 (CYP2D6) metabolizer status, gametocyte carriage, haptoglobin, lactate dehydrogenase levels and reticulocyte counts were also determined. In Burkina Faso, the mean maximum absolute change in hemoglobin was -2.13 g/dL (95% confidence interval [CI], -2.78, -1.49) in G6PDd individuals randomized to 0.25 PQ mg/kg and -2.29 g/dL (95% CI, -2.79, -1.79) in those receiving 0.40 PQ mg/kg. In The Gambia, the mean maximum absolute change in hemoglobin concentration was -1.83 g/dL (95% CI, -2.19, -1.47) in G6PDd individuals receiving 0.25 PQ mg/kg. After adjustment for baseline concentrations, hemoglobin reductions in G6PDd individuals in Burkina Faso were more pronounced compared to those in G6PD-normal individuals receiving the same PQ doses (P = 0.062 and P = 0.022, respectively). Hemoglobin levels normalized during follow-up. Abnormal haptoglobin and lactate dehydrogenase levels provided additional evidence of mild transient hemolysis post-PQ. Conclusions: Single low-dose PQ in combination with AL and DP was associated with mild and transient reductions in hemoglobin. None of the study participants developed moderate or severe anemia; there were no severe adverse events. This indicates that single low-dose PQ is safe in G6PDd African males when used with artemisinin-based combination therapy.
Low dose, high multiplicity thermolysin X-ray diffraction data from Diamond Light Source beamline I03
<p>Low dose, high multiplicity X-ray diffraction data recorded from a thermolysin crystal prepared according to standard protocols as part of ongoing research. The data were recorded with low transmission to ensure minimal radiation damage, with the side-effect that the individual reflections are exceedingly weak even at low resolution, and the majority of background pixels have no counts.</p>
Machine learning refinement of in situ images acquired by low electron dose LC-TEM
<p>This is a dataset of images acquired in vacuum condition and corresponding images in a solution listed in the text file list_dataset.txt. </p><p> </p><p>The code is available at <a href="https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM">this website</a>:</p><p>https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM/</p><p><br>Equipment of TEM:</p><ul><li>field-emission gun (JEM-2100F, JEOL, Tokyo)</li><li>OneView IS (Gatan, Inc., Pleasanton, CA, USA)</li></ul><p> </p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 1 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 1 and 2. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 (this one) <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
Low-dose biliatresone treatment of pregnant mice causes subclinical biliary disease in their offspring: Evidence for a spectrum of neonatal injury
<p>Biliary atresia is a neonatal disease characterized by damage, inflammation, and fibrosis of the liver and bile ducts and by abnormal bile metabolism. It likely results from a prenatal environmental exposure that spares the mother and affects the fetus. Our aim was to develop a model of fetal injury by exposing pregnant mice to low-dose biliatresone, a plant toxin implicated in biliary atresia in livestock, and then to determine whether there was a hepatobiliary phenotype in their pups.<strong> </strong>Pregnant mice were treated orally with 15 mg/kg/d biliatresone for 2 days. Histology of the liver and bile ducts, serum bile acids, and liver immune cells of pups from treated mothers were analyzed at P5 and P21. Pups had no evidence of histological liver or bile duct injury or fibrosis at either timepoint. In addition, growth was normal. However, serum levels of glycocholic acid were elevated at P5, suggesting altered bile metabolism, and the serum bile acid profile became increasingly abnormal through P21, with enhanced glycine conjugation of bile acids. There was also immune cell activation observed in the liver at P21. These results suggest that prenatal exposure to low doses of an environmental toxin can cause subclinical disease including liver inflammation and aberrant bile metabolism even in the absence of histological changes. This finding suggests a wide potential spectrum of disease after fetal biliary injury.</p>
TG 91: Radiation Risk Inference at Low-Dose and Low-Dose Rate Exposure for Radiological Protection Purposes
<p>The detriment-adjusted nominal risk coefficients recommended by ICRP have been based, to a large extent, on data obtained from the atomic bomb survivors in Japan. ICRP introduced a Dose and Dose-Rate Effectiveness Factor (DDREF) of 2 to include in calculations of these risk coefficients for application at the low doses and low-dose rate exposures typical in radiological protection. The DDREF was included to account for the survivors’ single acute exposure, and because a linear quadratic instead of a linear risk to dose response was thought to be the most plausible biological model.</p> <p>This paper summarizes the recent work of ICRP Task Group 91 on this topic. Task Group activities have included reviews of the history of low-dose and low-dose-rate effects, various exposure scenarios and their doses and dose rates, radiation-induced effects at the molecular, cellular, animal, and human level, and biologically-based mechanistic models of carcinogenesis. The Task Group performed a meta-analysis of low-dose-rate epidemiological studies, and re-analyses of published results on radiation-induced effects among animal models and among Japanese atomic bomb survivors.</p>
Figure 1 in Efficacy of low-dose EarthTec QZ treatment for the control of New Zealand mud snails Potamopyrgus antipodarum in a hatchery environment
Figure 1. Diagram of the split-plot study design.
TCIA full and low dose CT images and volumetric normal pancreas segmentations
<p>This publicly available CT dataset was downloaded from 50 portal venous phase abdominal CT scans (3mm-slice thickness) performed in 50 patients with liver metastases— from The Cancer Imaging Archive (TCIA).These CT scans were acquired with routine radiation dose levels per standard clinical protocols on SOMATOM Definition Flash CT scanner (Siemens Healthcare, Forchheim, Germany).Each study had been postprocessed to include a second reconstructed CT dataset at a simulated 25% radiation dose level. The mean (standard deviation [SD]) radiation dose for the full dose and the reduced dose CT datasets were 14.7 mSv (8 mSv) and 3.7 mSv (2 mSv), respectively. After a curation process by radiologists, 9 of 50 cases were excluded [chronic calcific pancreatitis (n = 1), postsurgical changes in pancreas (n = 2) and pancreatic lesions (n = 6)]. Volumetric pancreas segmentations were done on all the remaining 41 cases [16 males and 25 females, mean (SD) patient age: 60.8 years (14.7 yrs)] independently by the two radiologists (R1 and R2). Segmentations were first done on the full dose CT dataset and then repeated on the reduced dose CT dataset by both R1 and R2 after a gap of at least 24 hours to allow for memory extinction.</p>
Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwelltime and faster scans for high precision and low dose applications
<p>The data sets and the processing scripts are a part of the publication: <strong>Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwelltime and faster scans for high precision and low dose applications (</strong><a href="https://arxiv.org/abs/2107.02864">arXiv:2107.02864</a><strong>). </strong>The programming language used to process the data is written with python 3. </p> <p>In this work, a Timepix3 detector is used to perform 4D STEM experiments where instead of a frame based detection, a event driven approach is used. The experiments where performed on a probe-corrected FEI Themis Z with an acceleration voltage of 60 and 200 kV. The files provided show how to get the figures from the publication starting from the raw .t3p data. All intermediate data is added to reduce the computing time and give the ability to retrieve the images without the need to perform all the calculations.</p> <p>The .yml file contains the necessary package needed to perform the data analysis. For the single-sideband ptychographic reconstruction, the gitlab repository https://gitlab.com/pyptychostem/pyptychostem is used.</p> <p> </p> <p> </p> <p> </p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. Med Phys 2017;44(9):e125-e137.</li> <li>Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A. Learning-based occupational x-ray scatter estimation. Phys Med Biol 2022;67(7). doi: 10.1088/1361-6560/ac58dc</li> <li>Roser P, Birkhold A, Preuhs A, Syben C, Felsner L, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A. X-Ray Scatter Estimation Using Deep Splines. IEEE Trans Med Imaging 2021;40(9):2272-2283. doi: 10.1109/TMI.2021.3074712</li> <li>Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022;69(5):1608-1619. doi: 10.1109/TBME.2021.3123673</li> <li>Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupré GS, Gold GE, Fahrig R. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. II. Experiment. Med Phys 2014;41(6Part1):061902.</li> <li>Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, Beaupré GS, Maier A. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. Part I. Numerical modelbased optimization. Med Phys 2013;40(9):091905.</li> <li>Berger M, Muller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016;43(3):1235-1248. doi: 10.1118/1.4941012</li> <li>Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2021;67:101883. doi: 10.1016/j.media.2020.101883</li> <li>Preuhs A, Manhart M, Roser P, Hoppe E, Huang Y, Psychogios M, Kowarschik M, Maier A. Appearance Learning for Image-Based Motion Estimation in Tomography. IEEE Trans Med Imaging 2020;39(11):3667-3678. doi: 10.1109/TMI.2020.3002695</li> </ol>
Low vs. Standard Daily Doses of Antiepileptic Drugs in Newly Diagnosed, Previously Untreated Epilepsy(STANDLOW)
ClinicalTrials.gov study NCT03689114. IPD Sharing: NO. Countries: 1. Publications: 7.
Diagnosis of Acute Appendicitis: Low-dose Computed Tomography (CT) Versus Standard-dose CT
ClinicalTrials.gov study NCT00913380. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Use of Ribavirin and Low Dose Ara-C to Treat Acute Myeloid Leukemia
ClinicalTrials.gov study NCT01056523. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Prospective Low Dose CT for Total Shoulder Arthroplasty/Reverse Shoulder Arthroplasty
ClinicalTrials.gov study NCT05350319. IPD Sharing: NO. Countries: 1. Publications: 6.
ScienceDex guides
Understand access before you commit
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.