ACSys/RDN experiences with Telstra’s experimental broadband network, first progress report

This report summarises our experiences with the EBN and provides an indication of where we are now. We don’t present a set of detailed performance measurements in this report, instead we focus primarily on bandwidth utilisation and network management. We are currently producing a more comprehensive set of performance measurements, which will be presented in a subsequent report

Copernicus Global Land Cover Layers—Collection 2

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation

Beta-Blocker Therapy and Hemophagocytic Lymphohistiocytosis: A Case Report

Objective. The aim of this paper is to describe a fatal case of hemophagocytic lymphohistiocytosis (HLH) in a patient with severe heart failure, who was treated with low-dose propranolol. Patient and Interventions. We report on a 7-month-old boy with Downs syndrome who was born with an unbalanced, left dominant atrioventricular septal defect and aortic coarctation. Despite coarctation repair and pulmonary artery banding he developed intractable heart failure and fever of unknown origin. Since he remained in heart failure he received a trial of low-dose propranolol to stabilize his cardiopulmonary status, which resulted in unexpected immunomodulatory effects. Measurements and Main Result. Immunoactivation was evidenced by high concentrations of procalcitonin, soluble CD 25, tumor necrosis factor α, and interleukin 6 and 8. Propranolol resulting in hepatic compromise as indicated by high lactate dehydrogenase and alanine aminotransferase levels. A therapeutic switch from propranolol to the β1-receptor blocker metoprolol appeared to be instrumental in hemodynamic improvement and allowed discharge from hospital. However, the infant ultimately died from secondary inflammatory reactivation and intractable pulmonary obstructive disease. The autopsy results revealed HLH. Conclusion. Our case describes HLH secondary to heart failure and Downs syndrome. In this highly activated inflammatory state the beneficial hemodynamic effects of propranolol may be accompanied by immunomodulatory effects and the risk of acute liver failure. HLH occurs with a distinct pathophysiology, and specific treatment might be mandatory to increase the chance of survival

Nutzung von Resistenzmechanismen verschiedener Rebarten als Alternative zum Einsatz von Kupfer im Ökoweinbau

Extrakte aus Wildreben bzw. Hybriden mit hoher Resistenz: Es war zu prüfen, ob durch die Applikation von Blattextrakten von Nicht-Vitis-vinifera-Rebsorten auf Qualitätsrebsorten Plasmopara viticola und andere Pathogene bekämpft, unterdrückt oder pflanzeneigene Abwehrmechanismen bei Qualitätsrebsorten durch in den Extrakten enthaltene Elicitoren aktiviert werden können resp. ob mit den Pflanzenextrakten eine direkte Bekämpfung dieser Problemschaderreger möglich ist. Kupferreduktionspotential bei Anbau neuer Vitis vinifera PIWI - Sorten: Der Anbau von PIWI’s ermöglicht einen weitgehenden Verzicht auf Pflanzenschutz und ist ökologisch und ökonomisch die nachhaltigste Form des Weinbaus überhaupt. Das Kupfereinsparungspotential durch den Anbau von PIWI’s ist vermutlich abhängig von der Resistenz der Sorte und von den klimatischen Bedingungen am Standort. Das für die jeweilige Sorte notwendige Maß an Pflanzenschutz soll in diesem Projekt erstmals ermittelt werden. Daraus lässt sich ein durchschnittliches Einsparpotential an Kupfer bestimmen. Orientierungsversuche mit geringen Stockzahlen im Freiland an Zuchtstämmen mit bereits pyramidisierten Plasmopara Resistenzen wurden in einer 2009 erstellten Prüfanlage mit Überkronenberegnung durchgeführt, in der beliebig hohe Befallsbedingungen für die Rebenperonospora geschaffen werden können

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

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

Evaluation of ESA CCI prototype land cover map at 20m

In September 2017, the ESA CCI Land Cover Team released a prototype land cover (LC) map at 20 m resolution over Africa for the year 2016. This is the first LC map produced at such a high resolution covering an entire continent for the year 2016. To help improve the quality of this product, we have assessed its overall accuracy and identified regions where the map should be improved. We have compared the product against two independent datasets developed within the Copernicus Global Land Services (CGLS): a reference land cover dataset at a 10 m resolution, which has been used as training data to produce the LC map at 100 m over Africa for the year 2015 (http://land.copernicus.eu/global/products/lc); and an independent validation dataset at a 10 m resolution, which has been developed by CGLS for independent assessment of land cover maps at resolutions finer than 100 m. According to our estimates, overall accuracy of the African CCI LC at 20 m is approximately 65%. We have highlighted regions where the spatial distribution of such classes as shrubs, crops and trees should be improved before the map at 20 m could be used as input for research questions, e.g. conservation of biodiversity, crop monitoring and climate modelling