![enter image description here][1] <br> Candescence version 1.0 [software]<br> Varasana version 1.0 [training data]<br> Bettauer et al. (2022) [Microbiology Spectrum](https://journals.asm.org/doi/10.1128/spectrum.01472-22) Bettauer et al. (2021) [bioRxiv paper](https://www.biorxiv.org/content/10.1101/2021.06.10.445299v1) ---------- This sites serves as a repository for the **Candescence** software project. Candescence uses deep learning approaches to tackle different challenges related to the considerable array of morphologies for the opportunistic human fungal pathogen *Candida albicans*. This is a [short presentation](https://youtu.be/A6VpKFKpZA4) (12 minutes) of Candescence from our talk at Candida and Candidiasis 2021. Some movies of the GAN can be found [here](https://osf.io/qdxbp/wiki/Movies%20of%20GAN-based%20trajectories/) Varasana FCOS classifier. Candescence can identify C. albicans cells in microscopy images (DIC to date although we are working on other image types). This is constructed using a FCOS, a deep learning technique that efficiently locates objects and classifies each object in an image. Varasana GAN. Candescence also builds models of different morphologies and transitions between morphologies using a Generative Adversarial Network (GAN). This repository links to the following: 1. A Github respository which houses both the software used to train Candescence and the software that supported the manuscript. This is R and Python code. 2. Google Drive-based documents that include the manucript, presentations, figures, movies and a (Zotero-based) bibliography. 3. The annotations of the microscopy data for the training and validation components of the training set. The are also available at the Varasana temporary page. There are several projects that are based on Candescence. The code for these projects is also housed here in the candescence_master repository. This includes: (i) Candescence-grace, a collaboration with the [Leah Cowen](http://individual.utoronto.ca/cowen/) lab at the University of Toronto. There are two classifiers. The first detects Candida cells in tissue culture and labels them according to the degree of filamentation. The second detects Candida cells that have been internalized by macrophages, and labels each cell by the degree of filamentation. These classifiers were built using the GRACE library (Case and Westman (2023) manuscript in preparation). (ii) Candescence-guelph, a collaboration with the Rebecca Shapiro lab at the University of Guelph. Here we build a classifier to locate C. albicans cells and label them with the degree of filamentation. There are some important differences in the microscopy used in this project compared to those used to train Candescence-grace (Sharma et al. with J. Jacob from the Hallett lab (2023) manuscript in preparation). (iii) Candescence-tlv (Tel Aviv). This is a collaboration with the Judith Berman lab at Tel Aviv University. This involves the analysis of several thousand images of different strains of C. albicans to capture and interpret differences in filamentation. Currently both users and developers are required to install [MMDETECTION][6] on their machine. We build on top of this Python package. There are additional packages used by MMDETECTION that you will need to build the appropriate conda environment. The R code here uses version 4.0.4. There are installation instructions at the github site. P.S. The Candescence logo (above) is an 80s throwback thing, combining the aesthetic of that classic 1984 album Meat is Murder by The Smiths, with a hint of Joy Division's Love Will Tear Us Apart. ![enter image description here][8] Project led by the [Hallett lab][9]. ---------- [1]: https://files.osf.io/v1/resources/qdxbp/providers/osfstorage/60abd0eb0b6c6902339b782c?mode=render [2]: http://csfg-algonquin.concordia.ca/~hallett/candescence [3]: http://candescence.io [4]: http://csfg-algonquin.concordia.ca/~hallett/candescence [5]: https://www.ebi.ac.uk/bioimage-archive/ [6]: https://mmdetection.readthedocs.io/en/latest/#ne.tp://candescence.io [7]: http://mailto:hallett.mike.t@gmail.com [8]: https://files.osf.io/v1/resources/qdxbp/providers/osfstorage/60abd5f10b6c69022a9c0b4e?mode=render [9]: https://mikehallett.science