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- ItemReal-time image processing for label-free enrichment of Actinobacteria cultivated in picolitre droplets(London [u.a.] : Royal Society of Chemistry, 2013) Zang, E.; Brandes, S.; Tovar, M.; Martin, K.; Mech, F.; Horbert, P.; Henkel, T.; Figge, M.T.; Roth, M.The majority of today's antimicrobial therapeutics is derived from secondary metabolites produced by Actinobacteria. While it is generally assumed that less than 1% of Actinobacteria species from soil habitats have been cultivated so far, classic screening approaches fail to supply new substances, often due to limited throughput and frequent rediscovery of already known strains. To overcome these restrictions, we implement high-throughput cultivation of soil-derived Actinobacteria in microfluidic pL-droplets by generating more than 600000 pure cultures per hour from a spore suspension that can subsequently be incubated for days to weeks. Moreover, we introduce triggered imaging with real-time image-based droplet classification as a novel universal method for pL-droplet sorting. Growth-dependent droplet sorting at frequencies above 100 Hz is performed for label-free enrichment and extraction of microcultures. The combination of both cultivation of Actinobacteria in pL-droplets and real-time detection of growing Actinobacteria has great potential in screening for yet unknown species as well as their undiscovered natural products.
- ItemComputational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning(Washington, DC : OSA, 2021) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, ThomasHematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.