Effects of zinc on leaf decomposition by fungi in streams : studies in microcosms

The effect of zinc on leaf decomposition by aquatic fungi was studied in microcosms. Alder leaf disks were precolonized for 15 days at the source of the Este River, and exposed to different zinc concentrations during 25 days. Leaf mass loss, fungal biomass (based on ergosterol concentration), fungal production (rates of [1-14C]acetate incorporation into ergosterol), sporulation rates and species richness of aquatic hyphomycetes were determined. At the source of the Este River decomposition of alder leaves was fast and 50% of the initial mass was lost in 25 days. A total of 18 aquatic hyphomycete species were recorded during 42 days of leaf immersion. Articulospora tetracladia was the dominant species, followed by Lunulospora curvula and two unidentified species with sigmoid conidia. Cluster analysis suggested that zinc concentration and exposure time affected the structure of aquatic hyphomycete assemblages, even though richness had not been severely affected. Both zinc concentration and exposure time significantly affected leaf mass loss, fungal production and sporulation, but not fungal biomass. Zinc exposure reduced leaf mass loss, inhibited fungal production and affected fungal reproduction by either stimulating or inhibiting sporulation rates. The results of this work suggested zinc pollution might depress leaf decomposition in streams due to changes in the structure and activity of aquatic fungi.Fundação para a Ciência e a Tecnologia (FCT) – Programa Operacional “Ciência, Tecnologia, Inovação” (POCTI) - POCTI/34024/BSE/2000

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic