In 2015, I visited Fiji, Kiribati, and Tuvalu, which had just been hit by a cyclone. There, I learned a slogan -- "1.5 to stay alive" -- which refers to the 1.5 degrees Celsius (2.7 degrees Fahrenheit) threshold for global warming that, in theory, would avoid disastrous consequences. People living on the Pacific islands are well aware of the grave threat to humanity posed by climate change. Six months later, I met these new comrades again at climate negotiations in Paris. While speaking at an event, I referred to "1.5 to stay alive".

The main mission of the engineer is to participate in the project-based creation of automated image analysis tools. He will also guide and advise facility users in matters of image analysis, and promote the use of best practices in biological image analysis. Activities Design and implement tools for automated image analysis and processing using ImageJ/Fiji, java, python and other platforms. Set-up advanced workflows, including: image segmentation, quantification of intracellular protein distribution, pattern recognition, tracking of dynamic particles Train and integrate deep-learning methods into image analysis workflows Work with scientists to write and implement algorithms for solving image processing problems from multidimensional fluorescence microscopy datasets. While no formal biology training is needed, a strong interest in biology would facilitate the interaction with biologist users.

Object tracking --following objects over time --is an essential image analysis technique used to quantify dynamic processes in biosciences. A new application called TrackMate v7 enables scientists to track objects in images easily. TrackMate is a free, open-source tool available as part of the Fiji image analysis platform. In life sciences, tracking is used, for instance, to follow the movement of molecules, subcellular organelles, bacteria, cells, and whole animals. However, due to the sheer diversity of images used in research, no single application can address every tracking challenge.

Meiotic maturation is a crucial step of oocyte formation allowing its potential fertilization and embryo development. Elucidating this process is important both for fundamental research and assisted reproductive technology. Few computational tools based on non-invasive measurements are however available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images acquired in transmitted light. We trained neural networks to segment the contour of oocytes and their zona pellucida using oocytes from diverse species. We defined a comprehensive set of morphological features to describe an oocyte. These steps are implemented in an open-source Fiji plugin. We present a feature based machine learning pipeline to recognize oocyte populations and determine their morphological differences. We first demonstrate its potential to screen oocyte from different strains and automatically identify their morphological characteristics. Its second application is to predict and characterize the maturation potential of oocytes. We identify the texture of the zona pellucida and the cytoplasmic particles size as features to assess mouse oocyte maturation potential and tested whether these features were applicable to human oocyte's developmental potential.

DeepImageJ has been updated to DeepImageJ 2.1. The format of the models in previous versions are not compatible with DeepImageJ 2.1. Please, try to update your models using DeepImageJ Build Bundled Model or do not update DeepImageJ in Fiji using the Update Sites until you can update your models. Contact us if you have any question! DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments. Object tracking is an essential image analysis technique used across biosciences to quantify dynamic processes. In life sciences, tracking is used for instance to track single particles, sub-cellular organelles, bacteria, cells, and whole animals.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue.

We describe a very large improvement of existing hammer-style proof automation over large ITP libraries by combining learning and theorem proving. In particular, we have integrated state-of-the-art machine learners into the E automated theorem prover, and developed methods that allow learning and efficient internal guidance of E over the whole Mizar library. The resulting trained system improves the real-time performance of E on the Mizar library by 70% in a single-strategy setting.

We describe an efficient implementation of clause guidance in saturation-based automated theorem provers extending the ENIGMA approach. Unlike in the first ENIGMA implementation where fast linear classifier is trained and used together with manually engineered features, we have started to experiment with more sophisticated state-of-the-art machine learning methods such as gradient boosted trees and recursive neural networks. In particular the latter approach poses challenges in terms of efficiency of clause evaluation, however, we show that deep integration of the neural evaluation with the ATP data-structures can largely amortize this cost and lead to competitive real-time results. Both methods are evaluated on a large dataset of theorem proving problems and compared with the previous approaches. The resulting methods improve on the manually designed clause guidance, providing the first practically convincing application of gradient-boosted and neural clause guidance in saturation-style automated theorem provers.