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.

The promise of single-objective light-sheet microscopy is to combine the convenience of standard single-objective microscopes with the speed, coverage, resolution and gentleness of light-sheet microscopes. We present DaXi, a single-objective light-sheet microscope design based on oblique plane illumination that achieves: (1) a wider field of view and high-resolution imaging via a custom remote focusing objective; (2) fast volumetric imaging over larger volumes without compromising image quality or necessitating tiled acquisition; (3) fuller image coverage for large samples via multi-view imaging and (4) higher throughput multi-well imaging via remote coverslip placement. Our instrument achieves a resolution of 450 nm laterally and 2 μm axially over an imaging volume of 3,000 × 800 × 300 μm. We demonstrate the speed, field of view, resolution and versatility of our instrument by imaging various systems, including Drosophila egg chamber development, zebrafish whole-brain activity and zebrafish embryonic development – up to nine embryos at a time.

Deep Learning (DL) methods are increasingly recognised as powerful analytical tools for microscopy. Their potential to out-perform conventional image processing pipelines is now well established (1, 2). Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources, install multiple computational tools and modify code instructions to train neural networks all lead to an accessibility barrier that novice users often find difficult to cross. Here, we present ZeroCostDL4Mic, an entry-level teaching and deployment DL platform which considerably simplifies access and use of DL for microscopy. This is achieved by exploiting computational resources provided by Google Colab, a free, cloud-based service accessible through a web browser. ZeroCostDL4Mic allows researchers with little or no coding expertise to quickly train, assess and use popular DL networks. In parallel, it guides researchers to improve their understanding and to experiment with optimising DL parameters and network architectures. Altogether, ZeroCostDL4Mic accelerates the uptake of DL for new users and promotes their capacity to use increasingly complex DL networks.

Elucidating the developmental process of an organism will require the complete cartography of cellular lineages in the spatial, temporal, and molecular domains. We present Zebrahub, a comprehensive dynamic atlas of zebrafish embryonic development that combines single-cell sequencing time course data with light-sheet microscopy-based lineage reconstructions. Zebrahub is a foundational resource to study developmental processes at both transcriptional and spatiotemporal levels. It is publicly accessible as a web-based resource, providing an open-access collection of datasets and tools. Using this resource we shed new light on the pluripotency of Neuro-Mesodermal Progenitors (NMPs). We find that NMPs are pluripotent only during early axis elongation before becoming exclusively mesodermal progenitors. We attribute this restriction in NMP cell fate to emerging morphodynamic features that compartmentalize tissue motion.

Introductıon: We evaluated the eff ect of diff erent syringe volume, needle size and sample volume on blood gas analysis in syringes washed with heparin. Materials and methods:In this multi-step experimental study, percent dilution ratios (PDRs) and fi nal heparin concentrations (FHCs) were calculated by gravimetric method for determining the eff ect of syringe volume (1, 2, 5 and 10 mL), needle size (20, 21, 22, 25 and 26 G) and sample volume (0.5, 1, 2, 5 and 10 mL). The eff ect of diff erent PDRs and FHCs on blood gas and electrolyte parameters were determined. The erroneous results from nonstandardized sampling were evaluated according to RiliBAK's TEa. Results: The increase of PDRs and FHCs was associated with the decrease of syringe volume, the increase of needle size and the decrease of sample volume: from 2.0% and 100 IU/mL in 10 mL-syringe to 7.0% and 351 IU/mL in 1 mL-syringe; from 4.9% and 245 IU/mL in 26G to 7.6% and 380 IU/mL in 20 G with combined 1 mL syringe; from 2.0% and 100 IU/mL in full-fi lled sample to 34% and 1675 IU/mL in 0.5 mL suctioned sample into 10 mLsyringe. There was no statistical diff erence in pH; but the percent decreasing in pCO 2 , K + , iCa 2+ , iMg 2+ ; the percent increasing in pO 2 and Na + were statistical signifi cance compared to samples full-fi lled in syringes. The all changes in pH and pO 2 were acceptable; but the changes in pCO 2 , Na + , K + and iCa 2+ were unacceptable according to TEa limits except fullfi lled-syringes. Conclusions:The changes in PDRs and FHCs due nonstandardized sampling in syringe washed with liquid heparin give rise to erroneous test results for pCO 2 and electrolytes.