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Abstract. Arctic regions are under immense pressure from a continuously warming climate. During the winter and shoulder seasons, recently deglaciated sediments are particularly sensitive to human-induced warming. Understanding the physical mechanisms and processes that determine soil liquid moisture availability contributes to the way we conceptualize and understand the development and functioning of terrestrial Arctic ecosystems. However, harsh weather and logistical constraints limit opportunities to directly observe subsurface processes year-round; hence automated and uninterrupted strategies of monitoring the coupled heat and water movement in soils are essential. Geoelectrical monitoring using electrical resistivity tomography (ERT) has proven to be an effective method to capture soil moisture distribution in time and space. ERT instrumentation has been adapted for year-round operation in high-latitude weather conditions. We installed two geoelectrical monitoring stations on the forefield of a retreating glacier in Svalbard, consisting of semi-permanent surface ERT arrays and co-located soil sensors, which track seasonal changes in soil electrical resistivity, moisture, and temperature in 3D. One of the stations observes recently exposed sediments (5–10 years since deglaciation), whilst the other covers more established sediments (50–60 years since deglaciation). We obtained a 1-year continuous measurement record (October 2021–September 2022), which produced 4D images of soil freeze–thaw transitions with unprecedented detail, allowing us to calculate the velocity of the thawing front in 3D. At its peak, this was found to be 1 m d−1 for the older sediments and 0.4 m d−1 for the younger sediments. Records of soil moisture and thermal regime obtained by sensors help define the conditions under which snowmelt takes place. Our data reveal that the freeze–thaw shoulder period, during which the surface soils experienced the zero-curtain effect, lasted 23 d at the site closer to the glacier but only 6 d for the older sediments. Furthermore, we used unsupervised clustering to classify areas of the soil volume according to their electrical resistivity coefficient of variance, which enables us to understand spatial variations in susceptibility to water-phase transition. Novel insights into soil moisture dynamics throughout the spring melt will help parameterize models of biological activity to build a more predictive understanding of newly emerging terrestrial landscapes and their impact on carbon and nutrient cycling. 

Standard zebrafish transgenesis involves random transgene integration with resource-intensive screening. While phiC31 integrase–basedattP/attBrecombination has streamlined transgenesis in mice andDrosophila, validatedattP-based landing sites for universal applications are lacking in zebrafish. Here, we developedphiC31 Integrase Genomic Loci Engineered for Transgenesis(pIGLET) as transgenesis approach, with twoattPlanding sitespIGLET14aandpIGLET24bfrom well-validated Tol2 transgenes. Both sites facilitate diverse transgenesis applications including reporters and Cre/loxPtransgenes. ThepIGLET14aandpIGLET24blanding sites consistently yield 25 to 50% germline transmission, substantially reducing the resources needed for transgenic line generation. Transgenesis into these sites enables reproducible expression patterns in F0 zebrafish embryos for enhancer discovery and testing of gene regulatory variants. Together, our new landing sites streamline targeted, reproducible zebrafish transgenesis as a robust platform for various applications while minimizing the workload for generating transgenic lines. 

In this work, we investigated the classification of texture by neuromorphic tactile encoding and an unsupervised learning method. Additionally, we developed an adaptive classification algorithm to detect and characterize the presence of new texture data. The neuromorphic tactile encoding of textures from a multilayer tactile sensor was based on the physical structure and afferent spike signaling of human glabrous skin mechanoreceptors. We explored different neuromorphic spike pattern metrics and dimensionality reduction techniques in order to maximize classification accuracy while improving computational efficiency. Using a dataset composed of 3 textures, we showed that unsupervised learning of the neuromorphic tactile encoding data had high classification accuracy (mean=86.46%, sd=5 .44%). Moreover, the adaptive classification algorithm was successful at determining that there were 3 underlying textures in the training dataset. In this work, tactile information is transformed into neuromorphic spiking activity that can be used as a stimulation pattern to elicit texture sensation for prosthesis users. Furthermore, we provide the basis for identifying new textures adaptively which can be used to actively modify stimulation patterns to improve texture discrimination for the user.