No abstract

Optical aberrations due to refractive index mismatches occur in various types of microscopy due to refractive differences between the sample and the immersion fluid or within the sample. We study the effects of lateral refractive index differences by fluorescence confocal laser scanning microscopy due to glass or polydimethylsiloxane cuboids and glass cylinders immersed in aqueous fluorescent solution, thereby mimicking realistic imaging situations in the proximity of these materials. The reduction in fluorescence intensity near the embedded objects was found to depend on the geometry and the refractive index difference between the object and the surrounding solution. The observed fluorescence intensity gradients do not reflect the fluorophore concentration in the solution. It is suggested to apply a Gaussian fit or smoothing to the observed fluorescence intensity gradient and use this as a basis to recover the fluorophore concentration in the proximity of the refractive index step change. The method requires that the reference and sample objects have the same geometry and refractive index. The best results were obtained when the sample objects were also used for reference since small differences such as uneven surfaces will result in a different extent of aberration.

In the present study, we expand on the understanding of hydrogels with embedded deoxyribonucleic acid (DNA) cross-links, from the overall swelling to characterization of processes that precede the swelling. The hydrogels respond to target DNA strands because of a toehold-mediated strand displacement reaction in which the target strand binds to and opens the dsDNA cross-link. The spatiotemporal evolution of the diffusing target ssDNA was determined using confocal laser scanning microscopy (CLSM). The concentration profiles revealed diverse partitioning of the target DNA inside the hydrogel as compared with the immersing solution: excluding a nonbinding DNA, while accumulating a binding target. The data show that a longer toehold results in faster cross-link opening but reduced diffusion of the target, thus resulting in only a moderate increase in the overall swelling rate. The parameters obtained by fitting the data using a reaction-diffusion model were discussed in view of the molecular parameters of the target ssDNA and hydrogels.

The net swelling dynamics in molecular responsive hydrogels can be viewed as an integrated effect of discernible processes involving transport of actuating species, reaction with network components like destabilization of...

Remote sensing is a tool of interest for a large variety of applications. It is becoming increasingly more useful with the growing amount of available remote sensing data. However, the large amount of data also leads to a need for improved automated analysis. Deep learning is a natural candidate for solving this need. Change detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of change detection in multispectral remote-sensing images. It provides an overview of open datasets designed for change detection as well as a discussion of selected models developed for this task—including supervised, semi-supervised and unsupervised. Furthermore, the challenges and trends in the field are reviewed, and possible future developments are considered.