Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters

Common aquatic remote sensing algorithms estimate the trophic state (TS) of inland and nearshore waters through the inversion of remote sensing reflectance (Rrs ()) into chlorophyll-a (chla) concentration. In this study we present a novel method that directly inverts Rrs () into TS without prior chla retrieval. To successfully cope with the optical diversity of inland and nearshore waters the proposed method stacks supervised classification algorithms and combines them through meta-learning. We demonstrate the developed methodology using the waveband configuration of the Sentinel-3 Ocean and Land Colour Instrument on 49 globally distributed inland and nearshore waters (567 observations). To assess the performance of the developed approach, we compare the results with TS derived through optical water type (OWT) switching of chla retrieval algorithms. Meta-classification of TS was on average 6.75% more accurate than TS derived via OWT switching of chla algorithms. The presented method achieved 90% classification accuracies for eutrophic and hypereutrophic waters and was 12% more accurate for oligotrophic waters than derived through OWT chla retrieval. However, mesotrophic waters were estimated with lower accuracy from both our developed method and through OWT chla retrieval (52.17% and 46.34%, respectively), highlighting the need for improved base algorithms for low - moderate biomass waters. Misclassified observations were characterised by highly absorbing and/or scattering optical properties for which we propose adaptations to our classification strategy

EnMap In-flight Calibration Status

The Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite mission was successfully launched on 1st April 2022. The mission aims to monitor and characterise Earth’s environment in the spectral range from 420 - 2450 nm. The VNIR sensor provides 91 science channels ranging from 420 - 1000 nm with an average Spectral Sampling Distance (SSD) of 6.5 nm. While the SWIR sensor covers the range from 900 - 2450 nm with 131 channels and 10nm SSD. - The off-nadir pointing capability (up to 30 degrees) enables 5000 km to be monitored per day, with a swath width of 30 km and a spatial resolution of 30 m. The EnMAP satellite is equipped with several subsystems which allow periodic in-flight monitoring and calibration. The Full Aperture solar Diffuser Assembly (FADA) is used for absolute radiometric calibration. The On-Board Calibration Assembly (OBCA) is composed of 2 integrating spheres: one is coated with a doped diffuser material and is used for the spectral calibration; the second sphere, coated with a white spectralon, is used for Radiometric stability monitoring. Linearity LEDs are placed in front of the detector to monitor their linearity by measuring the response at constant illumination with increasing integration times. The Shutter Calibration Mechanism (SCM) allows for measurements with no light input to be performed in order to compute Dark Signal values and, in combination with Deep Space measurements, to compute any existing shutter emission in the SWIR range. This contribution will present a summary of the calibration activities performed during the EnMAP Commissioning Phase

Low avidity circulating SARS-CoV-2 reactive CD8+ T cells with proinflammatory TEMRA phenotype are associated with post-acute sequelae of COVID-19

The role of adaptive SARS-CoV-2 specific immunity in post-acute sequelae of COVID-19 (PASC) is not well explored, although a growing population of convalescent COVID-19 patients with manifestation of PASC is observed. We analyzed the SARS-CoV-2-specific immune response, via pseudovirus neutralizing assay and multiparametric flow cytometry in 40 post-acute sequelae of COVID-19 patients with non-specific PASC manifestation and 15 COVID-19 convalescent healthy donors. Although frequencies of SARS-CoV-2-reactive CD4+ T cells were similar between the studied cohorts, a stronger SARS-CoV-2 reactive CD8+ T cell response, characterized by IFNγ production and predominant TEMRA phenotype but low functional TCR avidity was detected in PASC patients compared to controls. Of interest, high avidity SARS-CoV-2-reactive CD4+ and CD8+ T cells were comparable between the groups demonstrating sufficient cellular antiviral response in PASC. In line with the cellular immunity, neutralizing capacity in PASC patients was not inferior compared to controls. In conclusion, our data suggest that PASC may be driven by an inflammatory response triggered by an expanded population of low avidity SARS-CoV-2 reactive pro-inflammatory CD8+ T cells. These pro-inflammatory T cells with TEMRA phenotype are known to be activated by a low or even without TCR stimulation and lead to a tissue damage. Further studies including animal models are required for a better understanding of underlying immunopathogensis. Summary: A CD8+ driven persistent inflammatory response triggered by SARS-CoV-2 may be responsible for the observed sequelae in PASC patients

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Remote Sensing of Cyanobacteria and Green Algae in the Baltic Sea

Eutrophication and the subsequent effects are one of the major ecological and economical problems in the Baltic Sea region. Two seasonal blooms, one dominated by green algae in spring and one dominated by blue-green algae in summer, form the phytoplankton cycle in the biggest brackish sea in the world. Anthropogenic nutrient input amplifies the phytoplankton growth. Cyanobacteria cultures dominating the summer blooms are not only capable of fixing atmospheric nitrogen and thereby play an important role in the nitrogen cycle, but are also potentially toxic. Dependent on a high water temperature, cyanobacteria also have a potential use as bio-indicator for climate change. Therefor, monitoring the occurrence and extent of different phytoplankton species is of high importance for understanding the ecosystem and human influence on it, as well as to examine possibilities of early warning systems. With its high CDOM concentrations, the Baltic Sea is a region with very specific optical properties, which demand for special regional algorithms, that take these properties into account. The German Aerospace Center (DLR) in Berlin has developed a new model-based inversion algorithm using neural network technique to derive four important water constituent parameters from MERIS satellite scenes over the Baltic Sea. Chlorophyll concentration as a proxy for green algae, phycocyanin absorption as a proxy for cyanobacteria, CDOM absorption and sediment scattering as further important parameters for the assessment of water quality. The algorithm shows good compliance with in-situ measured data from ships-of-opportunity, monitoring network data and a field campaign. Using atmospherically corrected MERIS reduced or full resolution scenes, an immediate calculation of analysis maps is possible by the implementation in an existing software environment