Cell interactions with hierarchically structured nano-patterned adhesive surfaces

The activation of well-defined numbers of integrin molecules in predefined areas by adhesion of tissue cells to biofunctionalized micro-nanopatterned surfaces was used to determine the minimum number of activated integrins necessary to stimulate focal adhesion formation. This was realized by combining micellar and conventional e-beam lithography, which enabled deposition of 6 nm large gold nanoparticles on predefined geometries. Patterns with a lateral spacing of 58 nm and a number of gold nanoparticles, ranging from 6 to 3000 per adhesive patch, were used. For αv β3-integrin activation, gold nanoparticles were coated with c(-RGDfK-)-thiol peptides, and the remaining glass surface was passivated to prevent non-specific protein adsorption and cell adhesion. Results show that focal adhesion formation is dictated by the underlying hierarchical nanopattern. Adhesive patches with side lengths of 3000 nm and separated by 3000 nm, or with side lengths of 1000 nm and separated by 1000 nm, containing approximately 3007 ± 193 or 335 ± 65 adhesive gold nanoparticles, respectively, induced the formation of actin-associated, paxillin-rich focal adhesions, comparable in size and shape to classical focal adhesions. In contrast, adhesive patches with side lengths of 500, 250 or 100 nm, and separated from adjacent adhesive patches by their respective side lengths, containing 83 ± 11, 30 ± 4, or 6 ± 1 adhesive gold nanoparticles, respectively, showed a significant increase in paxillin domain length, caused by bridging the pattern gap through an actin bundle in order to mechanically, synergistically strengthen each single adhesion site. Neither paxillin accumulation nor adhesion formation was induced if less than 6 c(-RGDfK-)-thiol functionalised gold nanoparticles per adhesion site were presented to cells

From sample to pixel: multi-scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large‐scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field‐collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 × 5 m2) remote sensing data as auxiliary data for upscaling field‐sampled aboveground carbon data to target (meso‐scale, i.e., 30 × 30 m2) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data—surface method, which uses a weighting layer, and regression method, which applies a regression model—with one method without auxiliary data—cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field‐based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non‐sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi‐scale remote sensing data

Nanopatterning by block copolymer micelle nanolithography and bioinspired applications

This comprehensive overview of block copolymer micelle nanolithography (BCMN) will discuss the synthesis of inorganic nanoparticle arrays by means of micellar diblock copolymer approach and the resulting experimental control of individual structural parameters of the nanopattern, e.g., particle density and particle size. Furthermore, the authors will present a combinational approach of BCMN with conventional fabrication methods, namely, photolithography and electron beam lithography, which combines the advantages of high-resolution micronanopatterning with fast sample processing rates. In addition, the authors will demonstrate how these nanoparticle assemblies can be transferred to polymer substrates with a wide range of elasticity. In the second part of this report the authors will introduce some of the most intriguing applications of BCMN in biology and materials science: The authors will demonstrate how nanoparticle arrays may be used as anchor points to pattern functional proteins with single molecule resolution for studying cellular adhesion and present a technological roadmap to high-performance nanomaterials by highlighting recent applications for biomimetic optics and nanowires. nt]mis|These authors contributed equally to this work

Estimating Grassland Parameters from Sentinel‑2: A Model Comparison Study

Grassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.Peer Reviewe

Hyperspectral satellite data for modelling spatial beta diversity patterns of birds along an environmental gradient

Human-driven reduction in biodiversity is widely acknowledged, with direct impact on ecosystem functioning and provisioning of services. However, existing patterns of biodiversity and most particularly those of community composition turnover, or beta diversity, are little known. While Earth observation missions provide an excellent tool for describing these patterns, the structural complexity of biotic communities is usually difficult to characterise using data from existing satellite sensors. Forthcoming hyperspectral missions will deliver much more detailed descriptions of the Earth's surface, which will greatly enhance our ability to tackle this issue. In the current study we used simulated EnMAP imagery, derived from geometrically and spectrally highly resolved airborne data from a region in southern Portugal. These data were used to describe the turnover of a bird community along an environmental gradient of shrub encroachment, resulting from land abandonment. For describing the turnover in community composition we adopted generalised dissimilarity modelling, while a sparse canonical correlation analysis enabled making full use of the hyperspectral information. The use of hyperspectral data, when compared to broadband multispectral data, such as Landsat TM, improved the explanatory power of the models by over 25%. Our results thus highlight the potential of hyperspectral satellite data for modelling the spatial patterns of biodiversity and ecosystem functioning. Nevertheless, further studies are still needed to validate the generalised usage of these type of data for tackling complex problems of ecosystem research

MOESM3 of Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna

Additional file 3. All partial dependency plots for PNCV (Chapada dos Veadeiros National Park, Brazil) for RFR models based on all available samples using the threshold 0.1. Metrics that relate to index values are shown in EVI * 10,000. Metrics related to time are shown as 8-day temporal bins starting from 01/01/2014