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Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare. [ABSTRACT FROM AUTHOR]

We present the generation and validation of an updated version of the TanDEM-X digital elevation model (DEM) of Antarctica: the TanDEM-X PolarDEM 90 m of Antarctica. Improvements compared to the global TanDEM-X DEM version comprise filling gaps with newer bistatic synthetic aperture radar (SAR) acquisitions of the TerraSAR-X and TanDEM-X satellites, interpolation of smaller voids, smoothing of noisy areas, and replacement of frozen or open sea areas with geoid undulations. For the latter, a new semi-automatic editing approach allowed for the delineation of the coastline from DEM and amplitude data. Finally, the DEM was transformed into the cartographic Antarctic Polar Stereographic projection with a homogeneous metric spacing in northing and easting of 90 m. As X-band SAR penetrates the snow and ice pack by several meters, a new concept for absolute height adjustment was set up that relies on areas with stable penetration conditions and on ICESat (Ice, Cloud, and land Elevation Satellite) elevations. After DEM generation and editing, a sophisticated height error characterization of the whole Antarctic continent with ICESat data was carried out, and a validation over blue ice achieved a mean vertical height error of just -0.3 m ± 2.5 m standard deviation. The filled and edited Antarctic TanDEM-X PolarDEM 90 m is outstanding due to its accuracy, homogeneity, and coverage completeness. It is freely available for scientific purposes and provides a high-resolution data set as basis for polar research, such as ice velocity, mass balance estimation, or orthorectification. [ABSTRACT FROM AUTHOR]

The Upper Parana Atlantic Forest (BAAPA) in Paraguay is one of the most threatened tropical forests in the world. The rapid growth of deforestation has resulted in the loss of 91% of its original cover. Numerous efforts have been made to halt deforestation activities, however farmers’ perception towards the forest and its benefits has not been considered either in studies conducted so far or by policy makers. This research provides the first multi-temporal analysis of the dynamics of the forest within the BAAPA region on the one hand, and assesses the way farmers perceive the forest and how this influences forest conservation at the farm level on the other. Remote sensing data acquired from Landsat images from 1999 to 2016 were used to measure the extent of the forest cover and deforestation rates over 17 years. Farmers’ influence on the dynamics of the forest was evaluated by combining earth observation data and household survey results conducted in the BAAPA region in 2016. Outcomes obtained in this study demonstrate a total loss in forest cover of 7500 km2. Deforestation rates in protected areas were determined by management regimes. The combination of household level and remote sensing data demonstrated that forest dynamics at the farm level is influenced by farm type, the level of dependency/use of forest benefits and the level of education of forest owners. An understanding of the social value awarded to the forest is a relevant contribution towards preserving natural resources. [ABSTRACT FROM AUTHOR]

The human–environment relationship within the Yellow River basin has a long history, because favorable environmental circumstances allowed the early emergence of societies along the river banks, and hence, the Yellow River basin was the birthplace of ancient Chinese civilization. On the other hand, the Yellow River is “China’s sorrow” due to the constant occurrences of flooding events throughout history. In recent decades, the Yellow River basin is facing a spectacular economic boom, but mainly achieved at the expenses of the environment by over-exploiting the natural resources provided within the basin, which causes various challenges on ecology and society. Water scarcity, pollution, and ecosystem degradation accompanied with biodiversity decline have been further aggravated by anthropogenic-induced climate change. To address the pressing socio-ecological challenges, various conservation and management plans and strategies have been issued, often consulted by international bodies. This article is a comprehensive overview of the current state and recent developments that have occurred in the Yellow River basin and presents and discusses current and pressing socio-ecological challenges. Additionally, we address different policy and management instruments that have been launched to ensure a long-term sustainable development within the basin. [ABSTRACT FROM AUTHOR]

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The Yellow River Basin is one of China's most densely-populated, fastest growing and most dynamic regions, with abundant natural resources and intense agricultural production. Major land policies have recently resulted in remarkable landscape modifications throughout the basin. The availability of precise regional land cover change information is crucial to better understand the prevailing dynamics and underlying factors influencing the current processes in such a complex system and can additionally serve as a valuable component for modeling and decision making. Such comprehensive and detailed information is lacking for the Yellow River Basin so far. In this study, we derived land cover characteristics and dynamics from the complete last decade based on optical high-temporal MODIS Normalized Differenced Vegetation Index (NDVI) time series for the whole Yellow River Basin. After filtering and smoothing for noise reduction with the use of the adaptive Savitzky-Golay filter, the processed time series was used to derive a large variety of phenological and annual metrics. The final classifications for the basin (2003 and 2013) were based on a random forest classifier, trained by reference samples from very high-resolution imagery. The accuracy assessment for all 18 thematic classes, which was based on a 30% reference data split, yielded an overall accuracy of 87% and 84% for 2003 and 2013, respectively. Major land cover and land use changes during the last decade have occurred on the Loess Plateau, where land and conservation reforms triggered large-scale recovery of grassland and shrubland habitat that had been previously covered by agriculture or sparse vegetation. Agricultural encroachment and urban area expansion are other processes influencing the dynamics in the basin. The necessity for regionally-adapted land cover maps becomes obvious when our land cover products are compared to existing global products, where thematic accuracy remains low, particularly in a heterogeneous landscape, such as the Yellow River Basin. The basin-wide novel land cover and land use products of the Yellow River Basin hold a large potential for climate, hydrology and biodiversity modelers, as well as river basin and regional governmental authorities and will be shared upon request. [ABSTRACT FROM AUTHOR]

Roadsides are habitats with very specific environmental conditions, often substantially differing from their natural surroundings. However, roads can have a positive effect on local vascular plant species richness. Endemic species on oceanic islands are considered to be less disturbance-adapted than native non-endemics and thus should be negatively affected by roads. Islands provide optimal conditions for testing this, as they possess a large share of clearly defined endemic species. This study focuses on a comparison of endemic plant species in roadside and surrounding communities and the interacting effects of elevation, vegetation type and trade wind-induced precipitation differences. We applied 96 circular plots with 50 m radius along two elevational gradients on the eastern (humid) and western (dry) slope of La Palma, Canary Islands, ranging from 100 to 2,400 m. Interestingly, we found roads to have a significant positive effect on endemic richness and the percentage of endemics as well as the same tendency for plant species richness after correcting for elevation and precipitation. Endemic species turnover was relatively high. The opening of cliffs during construction and, not to be overlooked, the protection from disturbances such as fire and omnipresent introduced herbivores (mainly rabbits or goats) probably leads to a positive effect of roads on endemics. In addition, many endemics might profit from species-specific dispersal capabilities well suited for roadside conditions. However, we do not argue for the use or even construction of roads for nature conservation but suggest protecting existing endemic populations because natural areas have a higher conservation value. [ABSTRACT FROM AUTHOR]

Since its launch in 2007, TerraSAR-X observations have been widely used in a broad range of scientific applications. Particularly in wetland research, TerraSAR-X’s shortwave X-band synthetic aperture radar (SAR) possesses unique capabilities, such as high spatial and temporal resolution, for delineating and characterizing the inherent spatially and temporally complex and heterogeneous structure of wetland ecosystems and their dynamics. As transitional areas, wetlands comprise characteristics of both terrestrial and aquatic features, forming a large diversity of wetland types. This study reviews all published articles incorporating TerraSAR-X information into wetland research to provide a comprehensive study of how this sensor has been used with regard to polarization, and the function of the data, time-series analyses, or the assessment of specific wetland ecosystem types. What is evident throughout this literature review is the synergistic fusion of multi-frequency and multi-polarization SAR sensors, sometimes optical sensors, in almost all investigated studies to attain improved wetland classification results. Due to the short revisiting time of the TerraSAR-X sensor, it is possible to compute dense SAR time-series, allowing for a more precise observation of the seasonality in dynamic wetland areas as demonstrated in many of the reviewed studies. [ABSTRACT FROM AUTHOR]