Similar Datasets
Project description:To understanding the miRNA expression profiling of cancer stem cells of laryngeal squamous carcinoma, the total RNA of CD133+CD44+ laryngeal cancer stem cells (isolated from LSCC cell line TU-177, named TDP), CD133-CD44- cells (TDN) and parental TU-177 (unsorted TU-177 cells, named TPT) was extracted, followed by miRNA sequencing. Differentially expressed miRNAs were identified.
2018-10-27 | GSE106280 | GEO
Project description:To understand the gene expression profiling of cancer stem cells of laryngeal squamous carcinoma, the total RNA of CD133+CD44+ laryngeal cancer stem cells (isolated from LSCC cell line TU-177, named TDP), CD133-CD44- cells (TDN) and parental TU-177 (unsorted TU-177 cells, named TPT) was extracted, followed by RNA sequencing. Differentially expression of lncRNA, mRNA, and circRNA was identified.
2018-10-27 | GSE106283 | GEO
Project description:To understand 4-TU labelling kinetics in a novel zebrafish transgenic line (lf:UPRT), we exposed adult lf:UPRT zebrafish (whereby animals have hepatocyte-specific uracil phosphoribosyltransferase expression) with 4-TU for 1, 3, 6 and 9 hours. We then dissected adult livers for SLAM-ITseq to determine the optimal 4-TU labelling time in our SLAM-ITseq workflow to allow us to study hepatocyte-specific nascent transcriptional changes.
2024-01-13 | GSE230830 | GEO
Project description:Understanding and predicting viewers' emotional responses to videos has emerged as a pivotal challenge due to its multifaceted applications in video indexing, summarization, personalized content recommendation, and effective advertisement design. A major roadblock in this domain has been the lack of expansive datasets with videos paired with viewer-reported emotional annotations. We address this challenge by employing a deep learning methodology trained on a dataset derived from the application of System1's proprietary methodologies on over 30,000 real video advertisements, each annotated by an average of 75 viewers. This equates to over 2.3 million emotional annotations across eight distinct categories: anger, contempt, disgust, fear, happiness, sadness, surprise, and neutral, coupled with the temporal onset of these emotions. Leveraging 5-second video clips, our approach aims to capture pronounced emotional responses. Our convolutional neural network, which integrates both video and audio data, predicts salient 5-second emotional clips with an average balanced accuracy of 43.6%, and shows particularly high performance for detecting happiness (55.8%) and sadness (60.2%). When applied to full advertisements, our model achieves a strong average AUC of 75% in determining emotional undertones. To facilitate further research, our trained networks are freely available upon request for research purposes. This work not only overcomes previous data limitations but also provides an accurate deep learning solution for video emotion understanding.
| S-EPMC11530461 | biostudies-literature
Project description:Background: In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear Methodology/Principal Findings: Here we have investigated HCC tumorigenesis by comparing gene expression (GSE25097), DNA copy number variation and survival using ~250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU. Conclusions/Significance: This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types
2011-07-05 | GSE28127 | GEO
Project description:A central problem in any quantum theory of gravity is to explain the emergence of the classical spacetime geometry in some limit of a more fundamental, microscopic description of nature. The gauge/gravity-correspondence provides a framework in which this problem can, in principle, be addressed. This is a holographic correspondence which relates a supergravity theory in five-dimensional Anti-deSitter space to a strongly coupled superconformal gauge theory on its 4-dimensional flat Minkowski boundary. In particular, the classical geometry should therefore emerge from some quantum state of the dual gauge theory. Here we confirm this by showing how the classical metric emerges from a canonical state in the dual gauge theory. In particular, we obtain approximations to the Sasaki-Einstein metric underlying the supergravity geometry, in terms of an explicit integral formula involving the canonical quantum state in question. In the special case of toric quiver gauge theories we show that our results can be computationally simplified through a process of tropicalization.
| S-EPMC8766586 | biostudies-literature
Project description:Neural-specific mRNA decay measurements by TU-Decay technique in control and Pumilio knockdown embryos
2015-04-02 | GSE67512 | GEO
Project description:Neural-specific mRNA decay measurements by TU-Decay technique in control and Pumilio knockdown embryos These TU-Decay microarrays analyze mRNA levels at three timepoints: a one hour pulse, one hour chase, and three hour chase. Neural-specific RNA purification was achieved using prospero-GAL4 driving UAS-T.g.UPRT. Pumilio knockdown in the nervous system was acheived using UAS-Pum(RNAi) driven by prospero-Gal4.
2015-04-02 | E-GEOD-67512 | biostudies-arrayexpress
Project description:Ancient beetle DNA from subfossil packrat middens
| PRJNA730090 | ENA