Elevated aluminium concentration in acidified headwater streams lowers aquatic hyphomycete diversity and impairs leaf-litter breakdown.

Aquatic hyphomycetes play an essential role in the decomposition of allochthonous organic matter which is a fundamental process driving the functioning of forested headwater streams. We studied the effect of anthropogenic acidification on aquatic hyphomycetes associated with decaying leaves of Fagus sylvatica in six forested headwater streams (pH range, 4.3-7.1). Non-metric multidimensional scaling revealed marked differences in aquatic hyphomycete assemblages between acidified and reference streams. We found strong relationships between aquatic hyphomycete richness and mean Al concentration (r = -0.998, p < 0.0001) and mean pH (r = 0.962, p < 0.002), meaning that fungal diversity was severely depleted in acidified streams. By contrast, mean fungal biomass was not related to acidity. Leaf breakdown rate was drastically reduced under acidic conditions raising the issue of whether the functioning of headwater ecosystems could be impaired by a loss of aquatic hyphomycete species

Cervical epithelial damage promotes Ureaplasma parvum ascending infection, intrauterine inflammation and preterm birth induction in mice

Around 40% of preterm births are attributed to ascending intrauterine infection, and Ureaplasma parvum (UP) is commonly isolated in these cases. Here we present a mouse model of ascending UP infection that resembles human disease, using vaginal inoculation combined with mild cervical injury induced by a common spermicide (Nonoxynol-9, as a surrogate for any mechanism of cervical epithelial damage). We measure bacterial load in a non-invasive manner using a luciferase-expressing UP strain, and post-mortem by qPCR and bacterial titration. Cervical exposure to Nonoxynol-9, 24 h pre-inoculation, facilitates intrauterine UP infection, upregulates pro-inflammatory cytokines, and increases preterm birth rates from 13 to 28%. Our results highlight the crucial role of the cervical epithelium as a barrier against ascending infection. In addition, we expect the mouse model will facilitate further research on the potential links between UP infection and preterm birth

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