Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comprehensive Dipteran Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification

ABSTRACT: This dataset contains over 20000 images of Dipteran wings collected, mostly of mosquitos. Each image is accompanied by extensive metadata, accessible in the 00_metadata.* files. The dataset aims to facilitate research in wing geometric morphometry and support the development of machine learning models for advancing vector surveillance and research. It is a retrospective collection, harmonizing contributions from research projects conducted between 2008 and 2026. The detailed metadata on each sample and image is available in the 00_metadata.*. This table below will provide an overview of the scope and range of the collected metadata. Due to the retrospective nature of the dataset, images may vary significantly in aspects such as lighting, background, and capture conditions across different projects. Although extensive efforts were made to ensure comprehensive metadata, some entries remain incomplete. Missing metadata entries are marked as MISSING VALUE," while fields intentionally left blank are indicated with "-". For the full description of the dataset we refer to the associated publication: https://www.nature.com/articles/s41597-025-05043-3#Sec5

SUBMITTER:

PROVIDER: S-BIAD1478 | bioimages |

REPOSITORIES: bioimages

ACCESS DATA

Json Xml

Similar Datasets

Image based identification and targeting of cancer stem cells in pancreatic adenocarcinoma

Project description:Pancreatic intraepithelial neoplasia (PanIN) is a premalignant lesion that can progress to pancreatic ductal adenocarcinoma, a highly lethal malignancy marked by its late stage at clinical presentation and profound drug resistance. Here we developed novel fluorescent reporter mice that show that the stem cell determinant, Musashi (Msi) is a critical element of pancreatic cancer progression. These reporters allowed functional and image based tracking of stem cell signals within cancers in vivo, revealing that Msi expression rises as PanINs progress to adenocarcinoma, and that Msi reporter+ tumor cells are the key drivers of pancreatic cancer. To visualize and track the function of live Msi-expressing cells in vivo, we developed Msi knock-in reporters in which fluorescent signals reflected endogenous Msi expression. This dataset represents pancreatic cancer_wt and pancreatic cancer_KO cells. Three wt reps and three KO reps.

2016-06-15 | E-GEOD-73312 | biostudies-arrayexpress

Image based identification and targeting of cancer stem cells in pancreatic adenocarcinoma

2016-06-15 | GSE73312 | GEO

Image-based identification and isolation of micronucleated cells to dissect cellular consequences

Project description:Recent advances in isolating cells based on visual phenotypes have transformed our ability to identify the mechanisms and consequences of complex traits. Micronucleus (MN) formation is a frequent outcome of genome instability, triggers extensive disease-associated changes in genome structure and signaling coincident with MN rupture, and is almost exclusively defined by visual analysis. Automated MN detection in microscopy images has proved extremely challenging, limiting unbiased discovery of the mechanisms and consequences of MN formation and rupture. In this study we describe two new MN segmentation modules: a rapid model for classifying micronucleated cells and their rupture status (VCS MN), and a robust model for accurate MN segmentation (MNFinder) from a broad range of fluorescence microscopy images. As a proof-of- concept, we define the transcriptome of non-transformed human cells with intact or ruptured MN after inducing chromosome missegregation by combining VCS MN with photoactivation-based cell isolation and RNASeq. Surprisingly, we find that neither MN formation nor rupture triggers a strong unique transcriptional response. Instead, transcriptional changes appear correlated with small increases in aneuploidy in these cell classes. Our MN segmentation modules overcome a significant challenge with reproducible MN quantification, and, joined with visual cell sorting, enable the application of powerful functional genomics assays, including pooled CRISPR screens and time-resolved analyses of cellular and genetic consequences, to a wide-range of questions in MN biology.

2025-04-15 | GSE291811 | GEO

Image based identification and targeting of cancer stem cells in pancreatic adenocarcinoma (PDAC)

Project description:Purpose: The goals of this study were to identify quantitative gene expression differences between wild type, Musashi1 null, Msuashi2 null and Musashi1/Musashi2 null MIAPaCa2 pancreatic cancer cells mRNA profiles of MIA PaCa-2 cancer cells were generated by deep sequencing, in triplicate, using Illumina HiSeq2500.

2016-06-15 | E-GEOD-75797 | biostudies-arrayexpress

Image based identification and targeting of cancer stem cells in pancreatic adenocarcinoma (PDAC)

2016-06-15 | GSE75797 | GEO

Image-based many-language programming language identification.

Project description: Not available

| S-EPMC8592246 | biostudies-literature

ImageDoubler: image-based doublet identification in single-cell sequencing.

Project description: Not available

| S-EPMC11695948 | biostudies-literature

Image based Machine Learning for identification of macrophage subsets.

Project description: Not available

| S-EPMC5471192 | biostudies-literature