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352 results for “Radiation effects”
A Study to Evaluate the Safety, Biodistribution, Internal Radiation Dosimetry, and Effective Dose of DaTSCAN™ Ioflupane (123I) Injection in Chinese Healthy Volunteers.
ClinicalTrials.gov study NCT04564092. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Probiotics Supplementation on the Side Effects of Radiation Therapy Among Colorectal Cancer Patients
ClinicalTrials.gov study NCT03742596. IPD Sharing: NO. Countries: 1. Publications: 9.
A Clinical Trial Evaluating the Effect of Pharmacological Ascorbate on Radiation Therapy for Pancreatic Cancer Patients
ClinicalTrials.gov study NCT03541486. IPD Sharing: YES. Countries: 1. Publications: 5.
Predictors of Tumor Response and of Radiation Therapy Side Effects in Patients With Gastrointestinal Cancers
ClinicalTrials.gov study NCT01445327. IPD Sharing: NO. Countries: 1. Publications: 3.
ROMAN: Phase 3 Trial Investigating the Effects of GC4419 on Radiation Induced Oral Mucositis in Head/Neck Cancer Patients
ClinicalTrials.gov study NCT03689712. IPD Sharing: NO. Countries: 3. Publications: 3.
Effect of Bevacizumab on Radiation-induced Brain Necrosis in Patients With Nasopharyngeal Carcinoma
ClinicalTrials.gov study NCT01621880. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Effect of Reflexology on Radiation-related Fatigue in Breast Cancer Patients
ClinicalTrials.gov study NCT00825682. IPD Sharing: NO. Countries: 1. Publications: 5.
MRI Scans in Evaluating the Effects of Radiation Therapy and Chemotherapy in Patients With Newly Diagnosed Glioblastoma Multiforme or Anaplastic Glioma
ClinicalTrials.gov study NCT00756106. IPD Sharing: NO. Countries: 1. Publications: 4.
A Study for Image-Guided Radiation Therapy in Pediatric Brain Tumors and Side Effects
ClinicalTrials.gov study NCT00187226. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of the Effects of GC4419 on Radiation Induced Oral Mucositis in Patients With Head/Neck Cancer
ClinicalTrials.gov study NCT02508389. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Supporting material for "Reappraisal of the effective radiative forcing of ozone-depleting substances"
<p>This is a companion repository, containing data and scripts needed to reproduce the figures and table entries in Morgenstern et al, Reappraisal of the effective radiative forcing of ozone-depleting substances, Geophysical Research Letters, 2020. For more information, please read the README.TXT file which is part of this repository.</p>
Data from: Crown asymmetry in high latitude forests: disentangling the directional effects of tree competition and solar radiation
Light foraging by trees is a fundamental process shaping forest communities. In heterogeneous light environments this behavior is expressed as plasticity of tree growth and the development of structural asymmetries. We studied the relative influence of neighborhood structure and directional solar radiation on horizontal asymmetry of tree crowns in late-successional high latitude (67–68°N) forests in northern Fennoscandia. We described crown asymmetries as crown vectors (i.e. horizontal vectors from stem center to crown center), which we obtained from canopy maps based on crown perimeter measurements in the field. To disentangle the influence of the two main determinants, inter-tree competition and directionality of above-canopy solar radiation at high latitudes, we applied circular statistical models, utilizing cylindrical distributions, to these data consisting of orientations and intensities of crown asymmetry. At the individual tree level, our model predicted crown asymmetry vectors from the current stand structure, and the predictions became better when the intensity of asymmetry (i.e. crown vector length) was higher. Competition was the main determinant of crown asymmetry for 2/3 of trees, and the model predictions improved when we incorporated the directionality of solar radiation. At the stand-level, these asymmetries had resulted in a small increment of the projected canopy area and an increased regularity of spatial structure. Our circular statistical modelling approach provided a quantitative evaluation of the relative importance of directionality of solar radiation and neighborhood stand structure, showing how both of these factors play a role in formation of crown asymmetries in high latitude forests. This approach further demonstrated the applicability of circular statistical modeling in ecological studies where the response variable has both orientation and intensity.
Data from: Effects of UVB radiation on grazing of two cladocerans from high-altitude Andean lakes
Climate change and water extraction may result in increased exposition of the biota to ultraviolet-B radiation (UVB) in high-altitude Andean lakes. Although exposition to lethal doses in these lakes is unlikely, sub-lethal UVB doses may have strong impacts in key compartments such as zooplankton. Here, we aimed at determining the effect of sub-lethal UVB doses on filtration rates of two cladoceran species (Daphnia pulicaria and Ceriodaphnia dubia). We firstly estimated the Incipient Limiting Concentration (ILC) and the Gut Passage Time (GPT) for both species. Thereafter we exposed clones of each species to four increasing UVB doses (treatments): i) DUV-0 (Control), ii) DUV-1 (0.02 MJ m2), iii) DUV-2 (0.03 MJ m2) and iv) DUV-3 (0.15 MJ m2); and estimated their filtration rates using fluorescent micro-spheres. Our results suggest that increasing sub-lethal doses of UVB radiation may strongly disturb the structure and functioning of high-altitude Andean lakes. Filtration rates of D. pulicaria were not affected by the lowest dose applied (DUV-1), but decreased by 50% in treatments DUV-2 and DUV-3. Filtration rates for C. dubia were reduced by more than 80% in treatments DUV-1 and DUV-2 and 100 % of mortality occurred at the highest UVB dose applied (DUV-3).
Data from: Spatial storage effect promotes biodiversity during adaptive radiation
Many ecological communities are enormously diverse. Variation in environmental conditions over time and space provides opportunities for temporal and spatial storage effects to operate, potentially promoting species coexistence and biodiversity. While several studies have provided empirical evidence supporting the significance of the temporal storage effect for coexistence, empirical tests of the role of the spatial storage effect are rare. In particular, we know little about how the spatial storage effect contributes to biodiversity over evolutionary timescales. Here, we report the first experimental study on the role of the spatial storage effect in the maintenance of biodiversity in evolving metacommunities, using the bacterium Pseudomonas fluorescens SBW25 as a laboratory model of adaptive radiation. We found that intercommunity spatial heterogeneity promoted phenotypic diversity of P. fluorescens in the presence of dispersal among local communities, by allowing the spatial storage effect to operate. Mechanistically, greater niche differences among P. fluorescens phenotypes arose in metacommunities with intercommunity spatial heterogeneity, facilitating negative frequency-dependent selection, and thus, the coexistence among P. fluorescens phenotypes. These results highlight the importance of the spatial storage effect for biodiversity over evolutionary timescales.
Model codes, data, and plot scripts for the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".
<p>The model codes, data, and plot scripts used in the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".</p><ul><li>Figs&NCL: the NCL scripts and figures used in the paper.</li><li>Mods: modified CAM model code.</li><li>PostFortran: the Fortran code for analysing the model results (post-processing) that produces the final results used for making plots.</li><li>Results4Plots: the final results used for making plots.</li></ul><p> </p>
Figure 2 in Radiation tolerance and bystander effects in the eutardigrade species Hypsibius dujardini (Parachaela: Hypsibiidae)
Figure 2. Radiation and radiation-induced bystander effects on the eutardigrade species Hypsibius dujardini. Survivorship curves following direct (RAD) and indirect (BYST) exposure to 3 and 5 kGy gamma radiation.
Figure 1 in Radiation tolerance and bystander effects in the eutardigrade species Hypsibius dujardini (Parachaela: Hypsibiidae)
Figure 1. Experimental set-up for testing radiation tolerance and bystander effects in the eutardigrade species Hypsibius dujardini: control (CON; 0 kGy), irradiated [received 3 kGy (RAD3) and 5 kGy (RAD5) gamma radiation] and bystander [exposed to an individual irradiated at the 3 kGy (BYST3) or 5 kGy (BYST5) level] groups.
Dataset of "Quantifying the Effects of Magnetic Field Line Curvature Scattering on Radiation Belt and Ring Current Particles"
<p>Dataset of "Quantifying the Effects of Magnetic Field Line Curvature Scattering on Radiation Belt and Ring Current Particles".</p><p>The following contains the simulation data used for drawing.</p>
Underlying data for: The Fe addition as an effective treatment for improving the radiation resistance of fcc NixFe1-x single-crystal alloys
<div> <p>The set contains 5 folders (TEM, SRIM, Nanoindentation, MC/MD simulations and RBSc_MSDA) containing raw test results for a specific method.</p> <p><strong>→ TEM</strong></p> <p>In TEM folder there are 2 sub-folders named “2e14 (0.5 dpa)” and “1e15 (12 dpa). In each sub-folder there are 8 original images that make up Figure 7 and Figure 8 in the paper. Below please find the description:</p> <p><strong>Fig.7.</strong> A) Cross-sectional TEM images of the Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 2×10<sup>14</sup> ions/cm<sup>2</sup> compared with SRIM calculations. B) Bright-field images of Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 4×10<sup>15</sup> ions/cm<sup>2</sup>. The red arrow indicates dislocation loops, green – defect clusters and yellow – SFT.</p> <p><strong>Fig.8.</strong> A) Cross-sectional TEM images of the Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 4×10<sup>15</sup> ions/cm<sup>2</sup> compared with SRIM calculations. B) Bright-field images of Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 4×10<sup>15</sup> ions/cm<sup>2</sup>. The red arrow indicates dislocation loops, blue – dislocation lines, green – defect clusters and yellow – SFT.</p> <p> </p> <p>To be able to reproduce Fig.9 and Fig.10 one needs images taken at 500k (attached in the files) and follow the instruction given in the article:</p> “Moreover, in Fig.9 B defect densities have been calculated to better understand the defect configuration for various compositions. Calculations were made based on the TEM images taken at the peak damaged region (at the highest magnification of 500k). For this measurement, lamellae thickness was also measured at the peak damage region only. The densities were calculated by counting the defect sizes in a unit volume of crystalline material (based on the same image where an average defect size was calculated and presented in Fig.8 A).”</div> <p>The lamella size was as follows:</p> <table> <tbody> <tr> <td> </td> <td>0.5 dpa</td> <td>12 dpa</td> </tr> <tr> <td> </td> <td>Lamella thickness</td> </tr> <tr> <td>Ni</td> <td>54</td> <td>117</td> </tr> <tr> <td>Ni<sub>0.62</sub>Fe<sub>0.38</sub></td> <td>56</td> <td>119</td> </tr> <tr> <td>Ni<sub>0.38</sub>Fe<sub>0.62</sub></td> <td>82</td> <td>83</td> </tr> </tbody> </table> <p>Surface area for all the materials [m<sup>2</sup>] - 1,08138E-13</p> <p> </p> <p><strong>→ SRIM</strong></p> <p>In SRIM folder there are two subfolders named: “Ni” NiFe62”. In each folder there are 3 .txt files (RANGE.txt file, VACANCY.txt and NOVAC.txt) that makes up the Fig. 1 in the paper.</p> <p>“The corresponding displacement per atom (dpa) profiles were predicted by the SRIM code for all elements using the full cascade mode. The dpa has been calculated based on the following equation according to recommendations of [28,29]:</p> <p> </p> <p><em>dpa = [fluence (ions/cm<sup>2</sup>) × total vacancies/A-ion × 10<sup>8</sup>] /atomic density (atoms/cm<sup>3</sup>) (1)</em></p> <p>“</p> <p>“The ion distribution was estimated from the RANGE.txt file. The corresponding dpa profiles were calculated using two files, VACANCY.txt and NOVAC.txt, under an assumed displacement energy threshold of 40 eV for all elements. The dpa profile is the sum of the vacancy concentrations using the column of “Knock-Ons” for Ni ions and the columns of “Vacancies” from target elements (the sum of Ni vacancies and Fe vacancies in the case of Ni<sub>x</sub>Fe<sub>1</sub><sub>−</sub><sub>x</sub>) in VACANCY.txt, together with the replacement collisions in NOVAC.txt. [31].”</p> <p> </p> <p><strong>→ Nanoindentation</strong></p> <p>In “Nanoindentation” folder there are four subfolders (“Fig.5 A – virgin multicycle”, “Fig.5 B – hardness versus fluence”, “Fig.5 C – LD curve 0.1 dpa”,” Fig.5 D – LD curve 12 dpa”), which appropriately reproduces the figures 5A, B, C and D. In folder “Fig.5 A – virgin multicycle” there is an excel file with all the data needed to reproduce Fig. 5 A. In folder “Fig.5 B – hardness versus fluence” there is an excel file with all the data needed to reproduce Fig. 5 B. There are bookmarks in excel “Ni”, “NiFe12”,”NiFe23”, “NiFe38”, “NiFe62”, where are the data obtained for each material and each fluence. Hardness value is obtained as sum of an average hardness obtained in the multicycle mode (at each particular load). In folder “Fig.5 C – LD curve 0.1 dpa” there are five .txt files needed to reproduce each of Load-Displacement curve at the damage level of 0,1 dpa (“LD Ni 0,1 dpa.txt”, “LD NiFe12 0,1 dpa.txt”, “LD NiFe23 0,1 dpa.txt”, “LD NiFe38 0,1 dpa.txt”, “LD NiFe62 0,1 dpa.txt”). In folder, ”Fig.5 D – LD curve 12 dpa” there are five .txt files needed to reproduce each of Load-Displacement curve at the damage level of 12 dpa (“LD Ni 12 dpa.txt”, “LD NiFe12 12 dpa.txt”, “LD NiFe23 12 dpa.txt”, “LD NiFe38 12 dpa.txt”, “LD NiFe62 12 dpa.txt”).</p> <p><strong>→ </strong><strong>MC/MD Simulations</strong></p> <p>In “MC/MD Simulations” folder there are two subfolders: “Fig. 6a” and “Fig. 6b”. In subfolder “Fig. 6a” there are 4 .txt files which make up Fig. 6a – “Ni38Fe62-swaps-energy.txt”, “Ni62Fe38-swaps-energy.txt”, “Ni77Fe23-swaps-energy.txt”, “Ni88Fe12-swaps-energy”. In subfolder “Fig. 6b” there are 4 .txt files which make up Fig. 6b – “Ni38Fe62-swaps-l12.txt”, “Ni62Fe38-swaps-l12.txt”, “Ni77Fe23-swaps-l12.txt”, “Ni88Fe12-swaps-l12.txt”. Moreover, in the main “MC/MD Simulations” folder one can find 4 movies (namely: “Ni38Fe62”, “Ni62Fe38”, “Ni77Fe23”, “Ni88Fe12“), which shows nanoprecipitation during hybrid MD-MC.</p> <p><strong>→ RBS/C_MSDA</strong></p> <p>In “RBS/C, MSDA” folder there is one origin .opj file “NiFe_implanted_Ni_2e14-2e15_rbs_1.62He_165degr”, in which one can find all the <u>experimentally obtained spectra</u>. “<em>These spectra </em><em>for pure Ni and Ni<sub>x</sub>Fe<sub>1-x</sub> alloys irradiated with different fluences were simulated using the Monte Carlo McChasy code developed at the NCBJ [30,32]. The energy of the backscattered particle can be directly related to the depth at which the close encounter scattering event occurred. The bulk scattering arises from particles that have been deflected atomic rows and have crossed over to another row, where they undergo a close-encounter event. To reveal the damage kinetics for investigated alloys the Multi-Step Damage Accumulation (MSDA) analysis was performed [33,34]. This model is based on the equation assuming that the damage accumulation occurs through a series of structural transformations caused by the destabilization of the present crystal structure</em>.”</p> <p>“<em>Points in the MSDA figure are corresponding to maximal values of extended defects formed in irradiated materials. Solid lines are the fits made following the MSDA equation [30,33,34]:</em></p> <p>f_{d} = \sum_{i=1}^{n}(f_{d, i}^{sat} - f_{d, i-1}^{sat})G[1-exp(\sigma_{i}(\Phi - \Phi_{i-1})))]</p> <p>where:</p> <p>\sigma_{i}<em> </em> - <em>cross-section for the formation of a given kind of defect</em></p> <p>f_{d, i}^{sat} <em>- level of damage at saturation for i-th kind of defects</em></p> <p>\Phi{i} <em>- fluence threshold for triggering the formation of i-th kind of defects “</em></p> <p> </p> <p> </p> <p> </p> <p><span>“Financial support from the National Science Centre, Poland through the </span><a href="https://www.sciencedirect.com/science/article/pii/S0169433224017045#gp010" target="_blank" rel="noopener noreferrer">PRELUDIUM 21</a><span> program in the frame of grant no. </span><a href="https://www.sciencedirect.com/science/article/pii/S0169433224017045#gp010" target="_blank" rel="noopener noreferrer">2022/45/N/ST5/02980</a><span> is gratefully acknowledged.”</span></p> <p> </p>
Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"
<p><strong>Overview</strong></p> <p>This archive contains models, data, and the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002550">Machine Learning Emulation of 3D Cloud Radiative Effects</a>".</p> <p>For the Python tool to generate synthetic data, please refer to the <a href="https://github.com/dmey/synthia">Synthia repository</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/singularity/">Singularity</a> (tested with version 3.6.3-1.el8)*.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler**.</li> </ul> <p>*Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p>**Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>).</p> <p><strong>Initialization</strong></p> <p>Deflate the data archive with:</p> <pre><code>./init.sh </code></pre> <p>Compile ecRad with Singularity:</p> <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <p><strong>Usage</strong></p> <p>To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder:</p> <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> <p>then, to plot stats and identify notebooks run:</p> <pre><code>qsub submit_stats.sh </code></pre> <p><strong>License</strong></p> <p>Paper code released under the <a href="./LICENSE.txt">MIT license</a>. Data released under <a href="./data/LICENSE.txt">CC BY 4.0</a>. <a href="https://confluence.ecmwf.int/display/ECRAD">ecRad</a> released under the <a href="./ecrad/LICENSE">Apache 2.0 license</a>.</p>
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.