A sample template to submit papers to MarXiv: the free research repository for the ocean and marine-climate sciences. Documentation for MarXiv is available at https://www.marxiv.org. The repository is located at https://osf.io/preprints/marxiv.
This template is based on the engrXiv template, accessible at https://www.overleaf.com/latex/templates/engrxiv-template/ttrnvgdkgcgy.
Template for writing scientific manuscripts. Features several examples of how to embed figures directly into the text. Use it to create compiled PDFs - e.g. for pre-peer review publication on the arXiv, bioRxiv, or to a repository such as figshare.
To submit your manuscript to the arXiv, bioRxiv, figshare or one of many other destinations linked to from Overleaf, simply click the 'Journals & Services' button on the top bar of the Overleaf editor and choose the appropriate destination from the menu. You can also use the 'Download as zip - for submission' option in the Project menu to download a zip file containing all the required files for the submission (e.g. including the .bbl file if you've used a bibliography file for your references).
Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.
Romain F. Laine, Kalina L. Tosheva, Nils Gustafsson, Robert D. M. Gray, Pedro Almada, David Albrecht, Gabriel T. Risa, Fredrik Hurtig, Ann-Christin Lindås, Buzz Baum, Jason Mercer, Christophe Leterrier, Pedro M. Pereira, Siân Culley, Ricardo Henriques