Cosmos bipinnatus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA341820 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:root Transcriptome of Cosmos bipinnata under cadmium stress
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Project description:Cosmos caudatus Transcriptome or Gene expression
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Project description:Dynamic of the Arabidopsis thaliana transcriptome following a cadmium exposition.<br> The goal of the project is to developp a global approach without a priori in order to identify the key players involved in response to cadmium: signalisation and mechanisms of detoxification in the model plant Arabidopsis thaliana. An originality of this project is to investigate the response of this organism by analyzing separately leaves and roots. This analysis will be performed in response to sub-toxic and toxic levels at different times. <br> Effects of two cadmium concentrations on leaves and roots at three different times.
2005-12-01 | E-MEXP-416 | biostudies-arrayexpress
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241834 | GEO
Project description:Lung cancer is the leading cause of cancer death worldwide. Low-dose computed tomography screening (LDCT) was recently shown to anticipate the time of diagnosis, thus reducing lung cancer mortality. We identifed a serum microRNA signature (the miR-Test) that could identify the optimal target population for LDCT screening. Here, we performed a large-scale validation study of the miR-Test in high-risk individuals enrolled in the Continuous Observation of Smoking Subjects (COSMOS) lung cancer screening program.
2016-02-29 | GSE76462 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241833 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241835 | GEO
Project description:Lung cancer is the leading cause of cancer death worldwide. Low-dose computed tomography screening (LDCT) was recently shown to anticipate the time of diagnosis, thus reducing lung cancer mortality. We identifed a serum microRNA signature (the miR-Test) that could identify the optimal target population for LDCT screening. Here, we performed a large-scale validation study of the miR-Test in high-risk individuals enrolled in the Continuous Observation of Smoking Subjects (COSMOS) lung cancer screening program. RT-qPCR of circulating microRNA purified from serum samples. Trizol-LS and miRNEASY Mini kit (Qiagen) were used for miRNA purification. Custom TaqMan® Low Density Array microRNA Custom Panel (Life Technologies) was used to screen serum circulating microRNA.
2016-02-29 | E-GEOD-76462 | biostudies-arrayexpress
Project description:Cardiac allograft vasculopathy (CAV) is the leading cause of mortality in heart transplant recipients. Despite the prevalence of CAV, there are no targeted therapeutic options to prevent or reverse disease progression, and patients ultimately require retransplant. CAV is characterized by progressive neointimal hyperplasia in donor heart coronary arteries, leading to luminal obliteration and ultimately allograft failure or sudden cardiac death. Although immune and stromal cell interactions are believed to play a key role in CAV pathogenesis, the specific cellular players and molecular signals driving disease remain undefined. In this study, we leverage single-cell RNA sequencing and spatial transcriptomics of human coronary arteries to transcriptionally characterize CAV and define the neointimal microenvironment. We compare arteries with CAV to atherosclerotic coronary artery disease and non-disease controls to identify a unique CAV transcriptional signature. Integration of single-cell RNA sequencing and spatial transcriptomic datasets revealed that modulated vascular smooth muscle cells and macrophage subsets dominate the CAV neointima and suggest that these cells interact to propagate type 1 interferon (IFN)-mediated inflammation. In a mouse CAV model, we demonstrate that interferon blockade with Ruxolitinib significantly reduced the incidence of CAV and prolonged allograft survival. Collectively, this study offers a novel and detailed characterization of the unique cellular and transcriptional landscape of CAV and identify a candidate pathway that may underly CAV pathogenesis, which could serve as a new therapeutic target for this devastating disease.
2026-03-06 | GSE316390 | GEO