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The problem of analyzing substances using low-cost sensors with a low signal-to-noise ratio (SNR) remains challenging. Using accurate models for the spectral data is paramount for the success of any classification task. We demonstrate that the thermal compensation of sample heating and spatial variability analysis yield lower modeling errors than non-spatial modeling. Then, we obtain the inference of the spectral data probability density functions using the integrated nested Laplace approximation (INLA) on a Bayesian hierarchical model. To achieve this goal, we use the fast and user-friendly R-INLA package in $R$for the computation. This approach allows affordable and real-time substance identification with fewer SNR sensor measurements, thereby potentially increasing throughput and lowering costs. 

Super-resolution microscopy is broadening our in-depth understanding of cellular structure. However, super-resolution approaches are limited, for numerous reasons, from utilization in longer-term intravital imaging. We devised a combinatorial imaging technique that combines deconvolution with stepwise optical saturation microscopy (DeSOS) to circumvent this issue and image cells in their native physiological environment. Other than a traditional confocal or two-photon microscope, this approach requires no additional hardware. Here, we provide an open-access application to obtain DeSOS images from conventional microscope images obtained at low excitation powers. We show that DeSOS can be used in time-lapse imaging to generate super-resolution movies in zebrafish. DeSOS was also validated in live mice. These movies uncover that actin structures dynamically remodel to produce a single pioneer axon in a “top-down” scaffolding event. Further, we identify an F-actin population – stable base clusters – that orchestrate that scaffolding event. We then identify that activation of Rac1 in pioneer axons destabilizes stable base clusters and disrupts pioneer axon formation. The ease of acquisition and processing with this approach provides a universal technique for biologists to answer questions in living animals.