Circumpolar Arctic vegetation: a hierarchic review and roadmap toward an internationally consistent approach to survey, archive and classify tundra plot data

Satellite-derived remote-sensing products are providing a modern circumpolar perspective of Arctic vegetation and its changes, but this new view is dependent on a long heritage of ground-based observations in the Arctic. Several products of the Conservation of Arctic Flora and Fauna are key to our current understanding. We review aspects of the PanArctic Flora, the Circumpolar Arctic Vegetation Map, the Arctic Biodiversity Assessment, and the Arctic Vegetation Archive (AVA) as they relate to efforts to describe and map the vegetation, plant biomass, and biodiversity of the Arctic at circumpolar, regional, landscape and plot scales. Cornerstones for all these tools are ground-based plant-species and plant-community surveys. The AVA is in progress and will store plot-based vegetation observations in a public-accessible database for vegetation classification, modeling, diversity studies, and other applications. We present the current status of the Alaska Arctic Vegetation Archive (AVA-AK), as a regional example for the panarctic archive, and with a roadmap for a coordinated international approach to survey, archive and classify Arctic vegetation. We note the need for more consistent standards of plot-based observations, and make several recommendations to improve the linkage between plot-based observations biodiversity studies and satellite-based observations of Arctic vegetation

The Alaska Arctic Vegetation Archive (AVA-AK)

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. We present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis

Global land characterisation using land cover fractions at 100 m resolution

Currently most global land cover maps are produced with discrete classes, which express the dominant land cover class in each pixel, or a combination of several classes at a predetermined ratio. In contrast, land cover fraction mapping enables expressing the proportion of each pure class in each pixel, which increases precision and reduces legend complexity. To map land cover fractions, regression rather than classification algorithms are needed, and multiple approaches are available for this task. A major challenge for land cover fraction mapping models is data sparsity. Land cover fraction data is by its nature zero-inflated due to how common the 0% fraction is. As regression favours the mean, 0% and 100% fractions are difficult for regression models to predict accurately. We proposed a new solution by combining three models: a binary model determines whether a pixel is pure; if so, it is processed using a classification model; otherwise with a regression model. We compared multiple regression algorithms and implemented our proposed three-step model on the algorithm with the lowest RMSE. We further evaluated the spatial and per-class accuracy of the model and demonstrated a wall-to-wall prediction of seven land cover fractions over the globe. The models were trained on over 138,000 points and validated on a separate dataset of over 20,000 points, provided by the CGLS-LC100 project. Both datasets are global and aligned with the PROBA-V 100 m UTM grid. Results showed that the random forest regression model reached the lowest RMSE of 17.3%. Lowest MAE (7.9%) and highest overall accuracy (72% ± 2%) was achieved using random forest with our proposed three-model approach and median vote. This research proves that machine learning algorithms can be applied globally to map a wide variety of land cover fractions. Fraction mapping expresses land cover more precisely, and empowers users to create their own discrete maps using user-defined thresholds and rules, which enables customising the result for a diverse range of uses. The three-step approach is useful for addressing the zero-inflation issue and mapping 0% and 100% fractions more accurately, and thus has already been taken up in the operational production of global land cover fraction layers within the CGLS-LC100 project. Furthermore, this study contributes to the accuracy assessment of land cover fraction maps both thematically and spatially, and these methods could be taken up by future land cover fraction mapping efforts

Towards operational validation of annual global land cover maps

Annual global land cover maps (GLC) are being provided by several operational monitoring efforts. However, map validation is lagging, in the sense that the annual land cover maps are often not validated. Concurrently, users such as the climate and land management community require information on the temporal consistency of multi-date GLC maps and stability in their accuracy. In this study, we propose a framework for operational validation of annual global land cover maps using efficient means for updating validation datasets that allow timely map validation according to recommendations in the CEOS Stage-4 validation guidelines. The framework includes a regular update of a validation dataset and continuous map validation. For the regular update of a validation dataset, a partial revision of the validation dataset based on random and targeted rechecking (areas with a high probability of change) is proposed followed by additional validation data collection. For continuous map validation, an accuracy assessment of each map release is proposed including an assessment of stability in map accuracy addressing the user needs on the temporal consistency information of GLC map and map quality. This validation approach was applied to the validation of the Copernicus Global Land Service GLC product (CGLS-LC100). The CGLS-LC100 global validation dataset was updated from 2015 to 2019. The update was done through a partial revision of the validation dataset and an additional collection of sample validation sites. From the global validation dataset, a total of 40% (10% for each update year) was revisited, supplemented by a targeted revision focusing on validation sample locations that were identified as possibly changed using the BFAST time series algorithm. Additionally, 6720 sample sites were collected to represent possible land cover change areas within 2015 and 2019. Through this updating mechanism, we increased the sampling intensity of validation sample sites in possible land cover change areas within the period. Next, the dataset was used to validate the annual GLC maps of the CGLS-LC100 product for 2015–2019. The results showed that the CGLS-LC100 annual GLC maps have about 80% overall accuracy showing high temporal consistency in general. In terms of stability in class accuracy, herbaceous wetland class showed to be the least stable over the period. As more operational land cover monitoring efforts are upcoming, we emphasize the importance of updated map validation and recommend improving the current validation practices and guidelines towards operational map validation so that long-term land cover maps and their uncertainty information are well understood and properly used

GeSn quantum wells as a platform for spin-resolved hole transport

The nascent group IV GeSn alloys are highly attractive for spintronics applications, including quantum computing, due to their ability to enable highly scalable fabrication and all-electrical spin manipulation. In this work, we conduct an in-depth study of a two-dimensional hole gas in a Ge/GeSn quantum well, exhibiting the integer quantum Hall effect and distinct Shubnikov-de Haas oscillations. Emphasis is given to the determination of the Landé g-factor and its pronounced anisotropy in this two-dimensional system, revealing values significantly higher than those in conventional Ge or SiGe/Ge systems. Moreover, by modeling the spin-orbit interaction using the Iordanskii-Lyanda-Geller-Pikus theory, crucial cubic Rashba spin-orbit interaction coefficients, are extracted and their significance is highlighted. This work provides the experimental validation of the theoretically predicted enhancements in spin-orbit interaction and g-factors in GeSn alloys compared to Ge. Additionally, it delivers essential parameters for the design of hole spin devices, such as hole qubits, utilizing GeSn-based structures on the Si platform

The Alaska Arctic Vegetation Archive (AVA-AK)

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. Here, we present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis

Tendon–bone contact pressure and biomechanical evaluation of a modified suture-bridge technique for rotator cuff repair

The aim of the study was to evaluate the time-zero mechanical and footprint properties of a suture-bridge technique for rotator cuff repair in an animal model. Thirty fresh-frozen sheep shoulders were randomly assigned among three investigation groups: (1) cyclic loading, (2) load-to-failure testing, and (3) tendon–bone interface contact pressure measurement. Shoulders were cyclically loaded from 10 to 180 N and displacement to gap formation of 5- and 10-mm at the repair site. Cycles to failure were determined. Additionally, the ultimate tensile strength and stiffness were verified along with the mode of failure. The average contact pressure and pressure pattern were investigated using a pressure-sensitive film system. All of the specimens resisted against 3,000 cycles and none of them reached a gap formation of 10 mm. The number of cycles to 5-mm gap formation was 2,884.5 ± 96.8 cycles. The ultimate tensile strength was 565.8 ± 17.8 N and stiffness was 173.7 ± 9.9 N/mm. The entire specimen presented a unique mode of failure as it is well known in using high strength sutures by pulling them through the tendon. We observed a mean contact pressure of 1.19 ± 0.03 MPa, applied on the footprint area. The fundamental results of our study support the use of a suture-bridge technique for optimising the conditions of the healing biology of a reconstructed rotator cuff tendon. Nevertheless, an individual estimation has to be done if using the suture-bridge technique clinically. Further investigation is necessary to evaluate the cell biological healing process in order to achieve further sufficient advancements in rotator cuff repair

Treatment of Peritoneal Carcinomatosis by Targeted Delivery of the Radio-Labeled Tumor Homing Peptide 213Bi-DTPA-[F3]2 into the Nucleus of Tumor Cells

BACKGROUND: Alpha-particle emitting isotopes are effective novel tools in cancer therapy, but targeted delivery into tumors is a prerequisite of their application to avoid toxic side effects. Peritoneal carcinomatosis is a widespread dissemination of tumors throughout the peritoneal cavity. As peritoneal carcinomatosis is fatal in most cases, novel therapies are needed. F3 is a tumor homing peptide which is internalized into the nucleus of tumor cells upon binding to nucleolin on the cell surface. Therefore, F3 may be an appropriate carrier for alpha-particle emitting isotopes facilitating selective tumor therapies. PRINCIPAL FINDINGS: A dimer of the vascular tumor homing peptide F3 was chemically coupled to the alpha-emitter (213)Bi ((213)Bi-DTPA-[F3](2)). We found (213)Bi-DTPA-[F3](2) to accumulate in the nucleus of tumor cells in vitro and in intraperitoneally growing tumors in vivo. To study the anti-tumor activity of (213)Bi-DTPA-[F3](2) we treated mice bearing intraperitoneally growing xenograft tumors with (213)Bi-DTPA-[F3](2). In a tumor prevention study between the days 4-14 after inoculation of tumor cells 6x1.85 MBq (50 microCi) of (213)Bi-DTPA-[F3](2) were injected. In a tumor reduction study between the days 16-26 after inoculation of tumor cells 6x1.85 MBq of (213)Bi-DTPA-[F3](2) were injected. The survival time of the animals was increased from 51 to 93.5 days in the prevention study and from 57 days to 78 days in the tumor reduction study. No toxicity of the treatment was observed. In bio-distribution studies we found (213)Bi-DTPA-[F3](2) to accumulate in tumors but only low activities were found in control organs except for the kidneys, where (213)Bi-DTPA-[F3](2) is found due to renal excretion. CONCLUSIONS/SIGNIFICANCE: In conclusion we report that (213)Bi-DTPA-[F3](2) is a novel tool for the targeted delivery of alpha-emitters into the nucleus of tumor cells that effectively controls peritoneal carcinomatosis in preclinical models and may also be useful in oncology