Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.

Accurate and efficient segmentation of live-cell images is critical in maximising data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labelling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.

Actively proliferating cells constantly monitor and readjust their metabolic pathways to ensure the replenishment of phospholipids necessary for membrane biogenesis and intracellular trafficking. In Saccharomyces cerevisiae, multiple studies have suggested that the lysine acetyltransferase complex NuA4 plays a role in phospholipid homeostasis. For one, NuA4 mutants induce the expression of the inositol-3-phosphate synthase gene, INO1, which leads to excessive accumulation of inositol, a key metabolite used for phospholipid biosynthesis. Additionally, NuA4 mutants also display negative genetic interactions with sec14-1ts, a mutant of a lipid-binding gene responsible for phospholipid remodeling of the Golgi. Here, using a combination of genetics and transcriptional profiling, we explore the connections between NuA4, inositol, and Sec14. Surprisingly, we found that NuA4 mutants did not suppress but rather exacerbated the growth defects of sec14-1ts under inositol-depleted conditions. Transcriptome studies reveal that while loss of the NuA4 subunit EAF1 in sec14-1ts does derepress INO1 expression, it does not derepress all inositol/choline-responsive phospholipid genes, suggesting that the impact of Eaf1 on phospholipid homeostasis extends beyond inositol biosynthesis. In fact, we find that NuA4 mutants have impaired lipid droplet levels and through genetic and chemical approaches, we determine that the genetic interaction between sec14-1ts and NuA4 mutants potentially reflects a role for NuA4 in fatty acid biosynthesis. Altogether, our work identifies a new role for NuA4 in phospholipid homeostasis.

Mathematical models provide a useful framework to investigate real-world problems. They can be used in the context of disease dynamics to study how a disease will spread and how we can stop or prevent an outbreak. In December of 2013, an outbreak of Ebola began in the West African country of Guinea and later spread to Sierra Leone and Liberia. Health Organisations like the US Centers for Disease Control and the World Health Organization were tasked with providing aid to end the outbreak. We create an SEIR compartmental model of Ebola with a fifth compartment for the infectious deceased to model the dynamics of an Ebola outbreak in a village of a thousand people. We analyse the disease-free equilibrium of the model and formulate an equation for the eradication threshold R 0. Sensitivity analyses points us in the direction of the transmission probability and the contact rate with infectious individuals as targets for intervention. We model the effect that vaccination and quarantine, together and separately, have on the outcome of the Ebola epidemic. We find that quarantine is a very effective intervention, but when combined with vaccination it can theoretically lead to eradication of the disease.