TY - JOUR A1 - Metzenmacher, Martin A1 - Váraljai, Renáta A1 - Hegedüs, Balazs A1 - Cima, Igor A1 - Forster, Jan A1 - Schramm, Alexander A1 - Scheffler, Björn A1 - Horn, Peter A. A1 - Klein, Christoph A. A1 - Szarvas, Tibor A1 - Reis, Hennig A1 - Bielefeld, Nicola A1 - Roesch, Alexander A1 - Aigner, Clemens A1 - Kunzmann, Volker A1 - Wiesweg, Marcel A1 - Siveke, Jens T. A1 - Schuler, Martin A1 - Lueong, Smiths S. T1 - Plasma Next Generation Sequencing and Droplet Digital-qPCR-Based Quantification of Circulating Cell-Free RNA for Noninvasive Early Detection of Cancer JF - Cancers N2 - Early detection of cancer holds high promise for reducing cancer-related mortality. Detection of circulating tumor-specific nucleic acids holds promise, but sensitivity and specificity issues remain with current technology. We studied cell-free RNA (cfRNA) in patients with non-small cell lung cancer (NSCLC; n = 56 stage IV, n = 39 stages I-III), pancreatic cancer (PDAC, n = 20 stage III), malignant melanoma (MM, n = 12 stage III-IV), urothelial bladder cancer (UBC, n = 22 stage II and IV), and 65 healthy controls by means of next generation sequencing (NGS) and real-time droplet digital PCR (RT-ddPCR). We identified 192 overlapping upregulated transcripts in NSCLC and PDAC by NGS, more than 90% of which were noncoding. Previously reported transcripts (e.g., HOTAIRM1) were identified. Plasma cfRNA transcript levels of POU6F2-AS2 discriminated NSCLC from healthy donors (AUC = 0.82 and 0.76 for stages IV and I–III, respectively) and significantly associated (p = 0.017) with the established tumor marker Cyfra 21-1. cfRNA yield and POU6F2-AS transcript abundance discriminated PDAC patients from healthy donors (AUC = 1.0). POU6F2-AS2 transcript was significantly higher in MM (p = 0.044). In summary, our findings support further validation of cfRNA detection by RT-ddPCR as a biomarker for early detection of solid cancers. KW - liquid biopsy KW - cfRNA KW - cancer KW - ddPCR KW - NGS KW - POU6F2-AS2 KW - early detection Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200553 SN - 2072-6694 VL - 12 IS - 2 ER - TY - JOUR A1 - Dech, Stefan A1 - Holzwarth, Stefanie A1 - Asam, Sarah A1 - Andresen, Thorsten A1 - Bachmann, Martin A1 - Boettcher, Martin A1 - Dietz, Andreas A1 - Eisfelder, Christina A1 - Frey, Corinne A1 - Gesell, Gerhard A1 - Gessner, Ursula A1 - Hirner, Andreas A1 - Hofmann, Matthias A1 - Kirches, Grit A1 - Klein, Doris A1 - Klein, Igor A1 - Kraus, Tanja A1 - Krause, Detmar A1 - Plank, Simon A1 - Popp, Thomas A1 - Reinermann, Sophie A1 - Reiners, Philipp A1 - Roessler, Sebastian A1 - Ruppert, Thomas A1 - Scherbachenko, Alexander A1 - Vignesh, Ranjitha A1 - Wolfmueller, Meinhard A1 - Zwenzner, Hendrik A1 - Kuenzer, Claudia T1 - Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience JF - Remote Sensing N2 - Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper. KW - AVHRR KW - Earth Observation KW - harmonization KW - time series analysis KW - climate related trends KW - automatic processing KW - Europe KW - TIMELINE Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246134 SN - 2072-4292 VL - 13 IS - 18 ER - TY - JOUR A1 - Mayr, Stefan A1 - Klein, Igor A1 - Rutzinger, Martin A1 - Kuenzer, Claudia T1 - Systematic water fraction estimation for a global and daily surface water time-series JF - Remote Sensing N2 - Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series. KW - earth observation KW - landsat KW - MODIS KW - remote sensing KW - probability KW - Sentinel-2 KW - subpixel KW - water Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242586 SN - 2072-4292 VL - 13 IS - 14 ER - TY - JOUR A1 - Mayr, Stefan A1 - Kuenzer, Claudia A1 - Gessner, Ursula A1 - Klein, Igor A1 - Rutzinger, Martin T1 - Validation of earth observation time-series: a review for large-area and temporally dense land surface products JF - Remote Sensing N2 - Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided. KW - accuracy KW - error estimation KW - global KW - intercomparison KW - remote sensing KW - uncertainty Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193202 SN - 2072-4292 VL - 11 IS - 22 ER -