Cosmos bipinnatus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA341820 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:root Transcriptome of Cosmos bipinnata under cadmium stress
| PRJNA321371 | ENA
Project description:Cosmos caudatus Transcriptome or Gene expression
| PRJNA486042 | ENA
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 | GSE241835 | 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: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:Bioassay is a system for monitoring toxicity of chemicals in the environment via the biological responses of experimental organisms. These responses can be detected by analysis of genome-wide changes in mRNA expression levels using DNA microarray. We applied this system for evaluation of synergistic toxicity by cadmium and thiuram, as this combination showed mutual growth inhibition in yeast. Hierarchical cluster analysis using the mRNA expression profiles suggested the response of yeast to this combination is similar to that seen with cadmium treatment. Functional characterization of induced genes by this combination treatment also suggests the enhanced toxicity of cadmium. This toxicity was observed as the damage to mitochondrial functions which were not observed with either cadmium or thiuram treatments alone. The potential toxicity to mitochondria by this combinational treatment was confirmed as the result of mitochondrial curing. We could evaluate the synergistic toxicity by cadmium and thiuram and show the possible use of transcriptome bioassay for synergistic toxicity. Keywords: stress response
2007-09-01 | GSE8718 | GEO