An ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiate (OC-CCI)

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea viewingWide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Remote magnetic navigation shows superior long-term outcomes in pediatric atrioventricular (nodal) tachycardia ablation compared to manual radiofrequency and cryoablation

Background: Catheter ablation (CA) is the first-choice treatment for tachyarrhythmia in children. Currently available CA techniques differ in mechanism of catheter navigation and energy sources. There are no large studies comparing long-term outcomes between available CA techniques in a pediatric population with atrioventricular reentry tachycardia (AVRT) or atrioventricular nodal reentry tachycardia (AVNRT) mechanisms. Objective: This study aimed to compare procedural and long-term outcomes of remote magnetic navigation-guided radiofrequency (RF) ablation (RMN), manual-guided RF ablation (MAN) and manual-guided cryoablation (CRYO). Methods: This single-center, retrospective study included all first consecutive CA procedures for AVRT or AVNRT performed in children without structural heart disease from 2008 to 2019. Three study groups were defined by the ablation technique used: RMN, MAN or CRYO. Primary outcome was long-term recurrence of tachyarrhythmia. Results: In total, we included 223 patients, aged 14 (IQR 12–16) years; weighting 56 (IQR 47–65) kilograms. In total, 108 procedures were performed using RMN, 76 using MAN and 39 using CRYO. RMN had significantly lower recurrence rates compared to MAN and CRYO at mean follow-up of 5.5 ± 2.9 years (AVRT: 4.3% versus 15.6% versus 54.5%, P < 0.001; AVNRT: 7.7% versus 8.3% versus 35.7%, P = 0.008; for RMN versus MAN versus CRYO respectively). In AVNRT ablation, RMN had significantly lower fluoroscopy doses compared to CRYO [30 (IQR 20–41) versus 45 (IQR 29–65) mGy, P = 0.040). Conclusion: In pediatric patients without structural heart disease who underwent their first AV(N)RT ablation, RMN has superior long-term outcomes compared to MAN and CRYO, in addition to favorable fluoroscopy doses

The First Evaluation of Remote Magnetic Navigation-Guided Pediatric Ventricular Arrhythmia Ablation

Catheter ablation (CA) is an important treatment option for ventricular arrhythmias (VA) in pediatric cardiology. Currently, various CA techniques are available, including remote magnetic navigation (RMN)-guided radiofrequency (RF) ablation. However, no studies evaluate RMN-guided ablative therapy outcomes in children with VA yet. This study aimed to compare procedural and long-term outcomes between RMN-guided and manual (MAN)-guided VA ablation in children. This single-center, retrospective study included all CA procedures for VA performed in children with or without structural heart disease from 2008 until 2020. Two study groups were defined by CA technique: RMN or MAN. Primary outcome was recurrence of VA. Baseline clinical, procedural and safety data were also evaluated. This study included 22 patients, who underwent 30 procedures, with a median age of 15 (IQR 14–17; range 1–17) years and a mean weight of 57 ± 20 kg. In total, 14 procedures were performed using RMN and 16 using MAN (22 first and 8 redo procedures). Regarding first procedures, recurrence rates were significantly lower in RMN compared to MAN (20% versus 67%, P = 0.029), at a mean follow-up of 5.2 ± 3.0 years. Moreover, fluoroscopy dosages were significantly lower in RMN compared to MAN [20 (IQR 14–54) versus 48 (IQR 38–62) mGy, P = 0.043]. In total, 20 patients (91%) were free of VA following their final ablation procedure. This is the first study to investigate the use of RMN in pediatric VA ablation. RMN showed improved outcomes compared to MAN, resulting in lower VA recurrence and reduced fluoroscopy exposure

Severe acute respiratory syndrome coronavirus 2 placental infection and inflammation leading to fetal distress and neonatal multi-organ failure in an asymptomatic woman

Background: In general, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy is not considered to be an increased risk for severe maternal outcomes but has been associated with an increased risk for fetal distress. Maternal-fetal transmission of SARS-CoV-2 was initially deemed uncertain; however, recently a few cases of vertical transmission have been reported. The intrauterine mechanisms, besides direct vertical transmission, leading to the perinatal adverse outcomes are not well understood. Methods: Multiple maternal, placental, and neonatal swabs were collected for the detection of SARS-CoV-2 using real-time quantitative polymerase chain reaction (RT-qPCR). Serology of immunoglobulins against SARS-CoV-2 was tested in maternal, umbilical cord, and neonatal blood. Placental examination included immunohistochemical investigation against SARS-CoV-2 antigen expression, with SARS-CoV-2 ribonucleic acid (RNA) in situ hybridization and transmission electron microscopy. Results: RT-qPCRs of the oropharynx, maternal blood, vagina, placenta, and urine were all positive over a period of 6 days, while breast milk, feces, and all neonatal samples tested negative. Placental findings showed the presence of SARS-CoV-2 particles with generalized inflammation characterized by histiocytic intervillositis with diffuse perivillous fibrin depositions with damage to the syncytiotrophoblasts. Conclusions: Placental infection by SARS-CoV-2 leads to fibrin depositions hampering fetal-maternal gas exchange with resulting fetal distress necessitating a premature emergency cesarean section. Postpartum, the neonate showed a fetal or pediatric inflammatory multisystem-like syndrome with coronary artery ectasia temporarily associated with SARS-CoV-2 for which admittance and care on the neonatal intensive care unit (NICU) were required, despite being negative for SARS-CoV-2. This highlights the need for awareness of adverse fetal and neonatal outcomes during the current coronavirus disease 2019 pandemic, especially considering that the majority of pregnant women appear asymptomatic

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel

Myeloid-derived suppressor cells control B cell accumulation in the central nervous system during autoimmunity

Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) have been characterized in the context of malignancies. Here we show that PMN-MDSCs can restrain B cell accumulation during central nervous system (CNS) autoimmunity. Ly6G+ cells were recruited to the CNS during experimental autoimmune encephalomyelitis (EAE), interacted with B cells that produced the cytokines GM-CSF and interleukin-6 (IL-6), and acquired properties of PMN-MDSCs in the CNS in a manner dependent on the signal transducer STAT3. Depletion of Ly6G+ cells or dysfunction of Ly6G+ cells through conditional ablation of STAT3 led to the selective accumulation of GM-CSF-producing B cells in the CNS compartment, which in turn promoted an activated microglial phenotype and lack of recovery from EAE. The frequency of CD138+ B cells in the cerebrospinal fluid (CSF) of human subjects with multiple sclerosis was negatively correlated with the frequency of PMN-MDSCs in the CSF. Thus PMN-MDSCs might selectively control the accumulation and cytokine secretion of B cells in the inflamed CNS