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10 results for “Scribble”
F I G U R E 2 in Mitochondrial phylogenomics of the Australian scribbly gum moth Ogmograptis (Lepidoptera: Bucculatricidae) and an examination of deep-level relationships within Lepidoptera
F I G U R E 2 Phylogeny of the Lepidoptera inferred from mitochondrial genomes, Part B—Apoditrysia. Topology and branch lengths are from the ML-PCG12-R analysis with nodal supports, maximum likelihood (ML) bootstraps (BS) and Bayesian inference (BI) posterior probabilities (PP) mapped for all eight analyses. Nodal supports depict the range of values: <70%/0.9, 70%–89%/0.9–0.94, 90%–99%/0.95–0.99 and 100%/1.0 (see key). Branch lengths are equal to expected substitutions/site. The complete trees for each analysis including precise nodal support values are included in Figures S7–S14.
F I G U R E 1 in Mitochondrial phylogenomics of the Australian scribbly gum moth Ogmograptis (Lepidoptera: Bucculatricidae) and an examination of deep-level relationships within Lepidoptera
F I G U R E 1 Phylogeny of the Lepidoptera inferred from mitochondrial genomes, Part A—non-Apoditrysia. Topology and branch lengths are from the ML-PCG12-R analysis with nodal supports, maximum likelihood (ML) bootstraps (BS) and Bayesian inference (BI) posterior probabilities (PP) mapped for all eight analyses. Nodal supports depict the range of values: <70%/0.9, 70%–89%/0.9–0.94, 90%–99%/0.95–0.99 and 100%/1.0 (see key). Branch lengths are equal to expected substitutions/site. The complete trees for each analysis including precise nodal support values are included in Figures S7–S14.
Dataset for IJCAI 2019 paper, Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
<p>The dataset and pre-trained model for IJCAI 2019 paper "Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks"</p> <ul> <li>Pre-trained model(Pytorch): DSPNet_G_200_epochs.pth</li> <li>Dataset (generated from coco dataset): starry_night_coco.zip</li> <li>Github: https://github.com/jinningli/DSP-Net</li> </ul> <p>Please cite our paper if you are using this dataset:</p> <p><em>Jinning Li, and Yexiang Xue. Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks. In International Joint Conference on Artificial Intelligence (IJCAI) 2019</em></p> <p>Abstract:</p> <p><em>We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.</em></p>
Identifying genes differentially expressed in MDCK cells with scribble knock-down (scribKD)
GEO Series GSE79042. Canis lupus familiaris. 17 samples. Type: Expression profiling by high throughput sequencing.
Scribble loss promotes pancreatic cancer development through intrinsic and extrinsic mechanisms
GEO Series GSE243939. Mus musculus. 15 samples. Type: Expression profiling by high throughput sequencing.
Gene expression change of MDCK cells expressing scribble shRNA in a tetracycline-inducible manner (scribKD cells)
GEO Series GSE153186. Canis lupus familiaris. 6 samples. Type: Expression profiling by array.
The International Spinal Cord Injury Blood Biomarker Longitudinal Evaluation (I-SCRIBBLE) Study
ClinicalTrials.gov study NCT06839300. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
"SCRIBBLE" Spinal Cord Injury Blood Biomarker Longitudinal Evaluation
ClinicalTrials.gov study NCT05244408. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Yap1-Scribble polarization is required for hematopoietic stem cell division and fate
GEO Series GSE151465. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Mislocalization of the cell polarity protein Scribble (Scrib) induces SPARC secretion in hepatocellular carcinoma
GEO Series GSE93742. Homo sapiens. 9 samples. Type: Expression profiling by array.
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DANDI Archive for NWB datasets
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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.