Highlights d Bacteria on the tongue form large organized consortia with a patch mosaic structure d Consortia are organized around a core of keratinized epithelial cells d Spatial organization can be quantified and permits inferences on dynamics

Highlights d Bacteria on the tongue form large organized consortia with a patch mosaic structure d Consortia are organized around a core of keratinized epithelial cells d Spatial organization can be quantified and permits inferences on dynamics

Ultraviolet radiation is known to be highly variable in aquatic ecosystems. It has been suggested that UV-exposed organisms may demonstrate enough phenotypic plasticity to maintain the relative fitness of natural populations. Our long-term objective is to determine the potential photoprotective effect of vitamin D3 on Daphnia pulex exposed to acute or chronic UV radiation. Herein we report our initial findings in this endeavor. D. pulex survival and reproduction (fitness) was monitored for 5 d as a proof of concept study. Significantly higher fitness was observed in the D. pulex with D3 than those without (most extreme effects observed were 0% survival in the absence of D3 and 100% with 10 ppm D3). Vitamin D3 was isolated from the culture media, the algal food (Pseudokirchneriella), and the D. pulex and quantified using high performance liquid chromatography (HPLC). Vitamin D3 was fluorescently labeled using a phenothiazinium dye and added to cultures of D. pulex. Images demonstrating the uptake of D3 into the tissues and carapace of the D. pulex were acquired. Our initial findings suggest a positive role for D3 in ecosystems as both UV-stressed algae and Daphnia sequester D3, and D. pulex demonstrate increased fitness in the presence of D3.

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Spectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images. We propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU was shown to have a similar reconstruction error but greatly improved proportion indeterminacy as compared to existing methods. The source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.