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  1. null (Ed.)
  2. Abstract

    The development of fluorophores with photoemission beyond 1000 nm provides the opportunity to develop novel fluorescence microscopes sensitive to those wavelengths. Imaging at wavelengths beyond the visible spectrum enables imaging depths of hundreds of microns in intact tissue, making this attractive for volumetric imaging applications. Here, a novel shortwave‐infrared line‐scan confocal microscope is presented that is capable of deep imaging of biological specimens, as demonstrated by visualization of labeled glomeruli in a fixed uncleared kidney at depths beyond 400 µm. Imaging of brain vasculature labeled with the near‐infrared organic dye indocyanine green, the shortwave‐infrared organic dye Chrom7, and rare earth‐doped nanoparticles is also shown, thus encompassing the entire spectrum detectable by a typical shortwave‐infrared sensitive InGaAs detector.

     
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  3. Summary

    We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial–temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared with discrete time processes in the setting of the present paper as well as other spatial–temporal situations.

     
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