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Dataset results
297 results for “pearls”
PEARL Program: Empowerment Program for Patients With Type 2 Diabetes (HK4)
ClinicalTrials.gov study NCT00950716. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PEEP Titration Guided by Electrical Impedance Tomography in Laparoscopic Surgery The PEaRL Study
ClinicalTrials.gov study NCT07289113. IPD Sharing: NO. Countries: 0. Publications: 7.
PEARL-SC Trial: A Study of the Efficacy, Safety, and Tolerability of A 623 Administration in Subjects With Systemic Lupus Erythematosus
ClinicalTrials.gov study NCT01162681. IPD Sharing: Not stated. Countries: 11. Publications: 2.
The Effect of Chewing the Tapioca Pearls in Bubble Tea Drinks
ClinicalTrials.gov study NCT04670341. IPD Sharing: NO. Countries: 1. Publications: 1.
Psilocybin-assisted Existential, Attachment and RelationaL (PEARL) Therapy for Patients With Advanced Cancer
ClinicalTrials.gov study NCT06416085. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Prenatal Education About Reducing Labor Stress (PEARLS)
ClinicalTrials.gov study NCT02327559. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Inference of domestication history and differentiation between early- and late-flowering varieties in pearl millet
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Data from: Crossing phenotype heritability and candidate gene expression in grafted black-lipped pearl oyster Pinctada margaritifera, an animal chimera
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Data from: Pearl millet and cowpea intercrop response to applied nutrients in West Africa
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Data from: Past and present dynamics of sorghum and pearl millet diversity in Mount Kenya region
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Data from: A parallel population genomic and hydrodynamic approach to fishery management of highly-dispersive marine invertebrates: the case of the Fijian black-lip pearl oyster Pinctada margaritifera
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Data from: Translocation of wild populations: conservation implications for the genetic diversity of the black-lipped pearl oyster Pinctada margaritifera
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Divergent northern and southern populations and demographic history of the pearl oyster in the western Pacific revealed with genomic SNPs
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Dataset for Disordered dolomite as an unusual biomineralization product found in the center of a natural Cassis pearl
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Multi-scale optical coherence tomography imaging and visualization of Vermeer's Girl with a Pearl Earring
<p>The data sets, software, and figures accompany the publication:</p> <p>"Multi-scale optical coherence tomography imaging and visualization of Vermeer's Girl with a Pearl Earring", Optics Express 28, 26239 (2020) <a href="https://doi.org/10.1364/OE.390703">https://doi.org/10.1364/OE.390703</a></p> <ul> <li>Fig4_largescaleOCT.pdf, high resolution image of figure 4 of the manuscript</li> <li>Fig5_mediumscaleOCT.pdf, high resolution image of figure 5 of the manuscript</li> <li>Fig6_smallscaleOCT.pdf, high resolution image of figure 6 of the manuscript</li> <li>GWPE_DEMO_V5.zip<br> Zip file of a Windows executable of the virtual rendering of the Girl with the Pearl Earring. The readme file describes the operation of the virtual rendering.</li> <li>Stitching_README.txt Describes the stitching software and the accompanying data sets</li> <li>MS-OCT surface data: <ul> <li>SurfaceStitchedKwadrant11.npy</li> <li>SurfaceStitchedKwadrant12.npy</li> <li>SurfaceStitchedKwadrant21.npy</li> <li>SurfaceStitchedKwadrant22.npy</li> <li>UpperInterfaceMatrixRawdataKwadrant11.npy</li> </ul> </li> <li>MS-OCT thickness data <ul> <li>FullResGlazeStratigraphyThicknessKwadrant11.npy</li> <li>FullResGlazeStratigraphyThicknessKwadrant12.npy</li> <li>FullResGlazeStratigraphyThicknessKwadrant21.npy</li> <li>FullResGlazeStratigraphyThicknessKwadrant22.npy</li> </ul> </li> <li>MS-OCT Scattering strength data <ul> <li>FullResScatteringStrengthKwadrant11.npy</li> <li>FullResScatteringStrengthKwadrant12.npy</li> <li>FullResScatteringStrengthKwadrant21.npy</li> </ul> </li> <li>Main.py, DataAnalysisFunctionsRepository.py, and ReadAndCombineKwadrantData.py are Python files for stitching the four quadrants of data together. Runs in Python >3.5. Full stitching image takes more than an hourFullResScatteringStrengthKwadrant22.npy</li> <li>Plot_measure.py and plot_fit.py<br> Analysis of the BRDF measurements of the black underlayer background (RU1) and glaze layer (RU5). Runs in Python > 3.5</li> <li>Visualization1.ave<br> Video of the multi-scale multi-parameter OCT data set of the Girl</li> <li>Visualization2.mp4<br> Video showing an impression of the interactive demo of the Girl <ul> </ul> </li> </ul>
Data from: Automated CT perfusion imaging for acute ischemic stroke: pearls and pitfalls for real world use
Recent positive trials have thrust acute cerebral perfusion imaging into the routine evaluation of acute ischemic stroke. Updated guidelines state that in patients with anterior circulation large vessel occlusions presenting beyond 6 hours from time last known well, advanced imaging selection including perfusion based selection is necessary. Centers that receive acute stroke patients must now have the capability to perform and interpret CT or MR perfusion imaging, or provide rapid transfer to centers with the capability of selecting patients for a highly impactful endovascular therapy, particularly in delayed time windows. Many stroke centers are quickly incorporating the use of automated perfusion processing software to interpret perfusion raw data. As CT perfusion is being assimilated in real world clinical practice, it is essential to understand the basics of perfusion acquisition, quantification and interpretation. It is equally important to recognize the common technical and clinical diagnostic challenges of automated CTP including ischemic core and penumbral misclassifications that could result in underestimation or overestimation of the core and penumbra volumes. This review highlights the pitfalls of automated CT perfusion along with practical pearls to address the common challenges. This is particularly tailored to aid the acute stroke clinician who must interpret automated perfusion studies, in an emergency setting to make time-dependent treatment decisions for acute ischemic stroke patients.
Data from: Genome scan reveals selection acting on genes linked to stress response in wild pearl millet
Uncovering genomic regions involved in adaption is a major goal in evolutionary biology. High-throughput sequencing now makes it possible to tackle this challenge in nonmodel species. Yet, despite the increasing number of methods targeted to specifically detect genomic footprints of selection, the complex demography of natural populations often causes high rates of false positive in gene discoveries. The aim of this study was to identify climate adaptations in wild pearl millet populations, Cenchrus americanus ssp. monodii. We focused on two climate gradients, one in Mali and one in Niger. We used a two-step strategy to limit false-positive outliers. First, we considered gradients as biological replicates and performed RNA sequencing of four populations at the extremities. We combined four methods—three based on differentiation among populations and one based on diversity patterns within populations—to identify outlier SNPs from a set of 87 218 high-quality SNPs. Among 11 155 contigs of pearl millet reference transcriptome, 540 exhibited selection signals as evidenced by at least one of the four methods. In a second step, we genotyped 762 samples in 11 additional populations distributed along the gradients using SNPs from the detected contigs and random SNPs as control. We further assessed selection on this large data set using a differentiation-based method and a method based on correlations with environmental variables based. Four contigs displayed consistent signatures between the four extreme and 11 additional populations, two of which were linked to abiotic and biotic stress responses.
Data from: Pearls & Oy-sters: challenging diagnosis of Gerstmann-Sträussler-Scheinker disease: clinical and imaging findings
PEARLS >Gerstmann-Straussler-Scheinker disease (GSS) is a rare prion disease characterized by cerebellar ataxia with progressive cognitive decline. >GSS is caused by a mutation within the prion protein gene (PRNP), which commonly exhibits autosomal dominant inheritance pattern. However, a significant portion of previously reported cases showed no family history of the disease, and GSS may also occur through de novo mutation of PRNP. OY-STERS >GSS is clinically heterogeneous and has no characteristic features on imaging. GSS could be considered in patients experiencing unexplained ataxia and subsequent cognitive decline even in those without a family history of the disease.
Data from: Population genomics of pearl millet (Pennisetum glaucum (L.) R. Br.): comparative analysis of global accessions and Senegalese landraces
Background: Pearl millet is a staple food for people in arid and semi-arid regions of Africa and South Asia due to its high drought tolerance and nutritional qualities. A better understanding of the genomic diversity and population structure of pearl millet germplasm is needed to support germplasm conservation and genetic improvement of this crop. Here we characterized two pearl millet diversity panels, (i) a set of global accessions from Africa, Asia, and the America, and (ii) a collection of landraces from multiple agro-ecological zones in Senegal. Results: We identified 83,875 single nucleotide polymorphisms (SNPs) in 500 pearl millet accessions, comprised of 252 global accessions and 248 Senegalese landraces, using genotyping by sequencing (GBS) of PstI-MspI reduced representation libraries. We used these SNPs to characterize genomic diversity and population structure among the accessions. The Senegalese landraces had the highest levels of genetic diversity (π), while accessions from southern Africa and Asia showed lower diversity levels. Principal component analyses and ancestry estimation indicated clear population structure between the Senegalese landraces and the global accessions, and among countries in the global accessions. In contrast, little population structure was observed across in the Senegalese landraces collections. We ordered SNPs on the pearl millet genetic map and observed much faster linkage disequilibrium (LD) decay in Senegalese landraces compared to global accessions. A comparison of pearl millet GBS linkage map with the foxtail millet (Setaria italica) and sorghum (Sorghum bicolor) genomes indicated extensive regions of synteny, as well as some large-scale rearrangements in the pearl millet lineage. Conclusions: We identified 83,875 SNPs as a genomic resource for pearl millet improvement. The high genetic diversity in Senegal relative to other regions of Africa and Asia supports a West African origin of this crop, followed by wide diffusion. The rapid LD decay and lack of confounding population structure along agro-ecological zones in Senegalese pearl millet will facilitate future association mapping studies. Comparative population genomics will provide insights into panicoid crop evolution and support improvement of these climate-resilient crops.
FIGURE 6 in A new species of Yunnanilus (Cypriniformes: Nemacheilidae) from upper Pearl River, Yunnan, China
FIGURE 6. Principal component analysis (PCA) results of Yunnanilus distributed in the Pearl River
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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.