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Was ist neu?Invasive Candida-Infektionen Invasive Mykosen spielen in der Intensivmedizin eine zunehmende Rolle. Das Erkennen von Risikopatienten und das richtige Diagnose- und Therapiemanagement spielen dabei die entscheidende Rolle.Influenza- und COVID-19-assoziierte pulmonale Aspergillose Neben der invasiven Candidiasis wurden zuletzt vermehrt Superinfektionen durch Schimmelpilze im Rahmen von schweren Influenza- und COVID-19-Infektionen beobachtet. Diese treten ohne die „klassischen“ Risikofaktoren für invasive Aspergillosen auf und bedürfen daher der besonderen Aufmerksamkeit der Intensivmediziner. Die Superinfektion mit Aspergillus-Spezies bei Influenza ist insofern neu, da bislang invasive Aspergillosen nur bei immunsupprimierten Patienten beschrieben wurden. Auch bei SARS-CoV-2 fanden sich schnell Berichte von COVID-19-assoziierten pulmonalen Aspergillosen, die möglicherweise zu einer Erhöhung der Mortalität führen können.

Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise selection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.

Since its first detection in China in late 2019 the novel coronavirus SARS-CoV-2 and the associated infectious disease COVID-19 continue to have a major impact on global health care and clinical practice. Cancer patients, in particular those with haematological malignancies, seem to be at an increased risk for a severe course of infection. Deliberations to avoid or defer potentially immunosuppressive therapies in these patients need to be balanced against the overarching goal of providing optimal antineoplastic treatment. This poses a unique challenge to treating physicians. This guideline provides evidence-based recommendations regarding prevention, diagnostics and treatment of SARS-CoV-2 infection and COVID-19 as well as strategies towards safe antineoplastic care during the COVID-19 pandemic. It was prepared by the Infectious Diseases Working Party (AGIHO) of the German Society for Haematology and Medical Oncology (DGHO) by critically reviewing the currently available data on SARS-CoV-2 and COVID-19 in cancer patients applying evidence-based medicine criteria., Highlights • Cancer patients are vulnerable to SARS-CoV-2 infection and severe COVID-19. • Comprehensive management strategies are required in care for cancer patients. • Evidence-based recommendations help clinicians make informed treatment decisions. • Providing state-of-the-art cancer care is possible during COVID-19 pandemic.

The retrieval of solar-induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and Orbiting Carbon Observatory-3 (OCO-3) platforms, which are provided for each platform in daily netCDF files (Frankenberg, 2022, https://doi.org/10.22002/D1.8771; OCO-2 Science Team et al., 2020, https://doi.org/10.5067/XO2LBBNPO010; OCO-3 Science Team et al., 2020, https://doi.org/10.5067/NOD1DPPBCXSO). We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users with the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution of spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes.

Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., normalized difference vegetation index, NDVI, near-infrared reflectance of terrestrial vegetation, NIRv, and kernel normalized difference vegetation index, kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution, while many processes influencing GPP occur at fine spatial scales. Here, we develop a convolutional neural network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high-resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r2 and RMSE metrics of 0.92 and 0.17 mW m−2 sr−1 nm−1, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (together OCO-2/3). SIFnet performs systematically better than the downscaling approach (r=0.78 for SIFnet, r=0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions in which these parameters matter. Namely, the CNN finds low-resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIRv consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high-spatial-resolution SIF data.

[EN] The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743¿758¿nm window. A secondary SIF dataset derived from an extended fitting window (735¿758¿nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3¿nm windows devoid of atmospheric absorption within the 665¿785¿nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier, This research has been supported by the European Space Agency (grant no. 4000127461/19/I-NS) and the National Aeronautics and Space Administration (grant no. NNX15AH95G)

Methods developed to explore the luminescent properties of the moon facilitated the development of techniques to infer terrestrial solar-induced chlorophyll fluorescence (SIF) from satellite instruments. While single SIF retrievals are inherently noisy, averaging many retrievals allows us to obtain highly accurate estimates. We analyzed several years of aggregated SIF data collected by the TROPOspheric Monitoring Instrument (TROPOMI) and the Orbiting Carbon Observatory-2 (OCO-2) over nonvegetated areas to explore the potential of SIF retrievals beyond the realm of photosynthesis. The fundamentally different retrievals at varying wavelengths in the near-infrared reveal that about 10% of all barren surfaces are weakly luminescent, while a few areas luminesce strongly—amounts comparable to SIF from vegetation. By means of lithological maps, we attributed the strongest luminescence signals to exposed carbonate sedimentary rocks. Besides a detailed evaluation of the signal properties, we discussed implications for SIF data sets and other remote sensing products.

The retrieval of solar induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and OCO-3 platforms, which are provided for each platform in daily netCDF files. We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun-sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes.

The Geostationary Carbon Cycle Observatory (GeoCarb), to be launched in 2023, will be capable of measuring solar-induced chlorophyll fluorescence (SIF) and add to the current space-based record which started in 1995. GeoCarb will be unique as it will be the first geostationary satellite capable of sensing SIF over the American continents. Consequently, SIF measurements from GeoCarb can be performed much more flexibly compared to polar-orbiting platforms. With its scan mirror assembly, the instrument can point to any location on the Earth disc. This will allow measurements to be collected at various times of day, and measurement locations can be re-visited several times within a day. In expectation of the launch, we conduct an extensive, SIF-focused simulation study and explore the capability and limitations of the instrument and its particular sampling approach. Using cloud information from real measurements, as well as other observation- and model-based data, we produce over four million atmospheric simulations of GeoCarb measurements that the instrument would see throughout a full day. We then apply dedicated SIF retrieval algorithms on the simulated spectra and investigate the results along with cloud-screening performance and emergent retrieval biases and subsequent bias correction. Finally, we make comparisons with currently operating instruments where appropriate and show future science users of GeoCarb SIF what a typical day of measurements will yield.

Traditional methods of carbon monitoring in mountainous regions are challenged by complex terrain. Recently, solar‐induced fluorescence (SIF) has been found to be an indicator of gross primary production (GPP), and the increased availability of remotely sensed SIF provides an opportunity to estimate GPP across the Western United States. Although the empirical linkage between SIF and GPP is strong, the current mechanistic understanding of this linkage is incomplete and depends upon changes in leaf biochemical processes in which absorbed sunlight leads to photochemistry, heat (via nonphotochemical quenching [NPQ]), fluorescence, or tissue damage. An improved mechanistic understanding is necessary to leverage SIF observations to improve representation of ecosystem processes within land surface models. Here we included an improved fluorescence model within the Community Land Model, Version 4.5 (CLM 4.5), to simulate seasonal changes in SIF at a subalpine forest in Colorado. We found that when the model accounted for sustained NPQ, this provided a larger seasonal change in fluorescence yield leading to simulated SIF that more closely resembled the observed seasonal pattern (Global Ozone Monitoring Experiment‐2 [GOME‐2] satellite platform and a tower‐mounted spectrometer system). We found that an acclimation model based on mean air temperature was a useful predictor for sustained NPQ. Although light intensity was not an important factor for this analysis, it should be considered before applying the sustained NPQ and SIF to other cold climate evergreen biomes. More leaf‐level fluorescence measurements are necessary to better understand the seasonal relationship between sustained and reversible components of NPQ and to what extent that influences SIF.

The new TROPOspheric Monitoring Instrument (TROPOMI) solar-induced chlorophyll fluorescence (SIF) data provides new opportunities to corroborate and improve global photosynthesis estimates. Here we report the spatiotemporal consistency between TROPOMI SIF and vegetation indices from the bidirectional reflectance distribution function (BRDF) adjusted (MCD43) and standard MODIS (MOD09) surface reflectance products, estimates of absorbed photosynthetically active radiation by chlorophyll (APAR_(chl)) derived from National Centers for Environmental Prediction Reanalysis-2 (NCEP2), MODIS MCD18, and European Reanalysis (ERA5) data, and two GPP products (GPP_(VPM) and GPP_(MOD17)). We find (a) non-adjusted VIs were more highly correlated with SIF at mid and high latitude than BRDF-adjusted VIs, but were less correlated in the tropics, (b) negligible differences in the correlation between SIF and non-adjusted NIRv and EVI, but BRDF-adjusted NIRv had higher correlations with SIF at mid to high latitude than BRDF-adjusted EVI but lower correlations in the tropics, (c) choice of PAR data set likely to cause substantial differences in global and regional GPP estimates and the correlation between modeled GPP and SIF, (d) SIF was more highly correlated with APAR_(chl) at high to mid latitude than EVI but more highly correlated with EVI at lower latitudes, and (e) GPP_(VPM) is more highly correlated with SIF than GPP_(MOD17), except in sub-Sahara Africa. Our results highlight that spaceborne photosynthesis would likely be improved by using a non-linear response to PAR and that the fundamental differences between the vegetation indices and PAR data sets are likely to yield important differences in global and regional estimates of photosynthesis.

At the leaf level, stomata control the exchange of water and carbon across the air-leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, it has not been evaluated how well the optimization models perform at larger scales. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves or the entire canopy. As a result, they are unable to evaluate the impact of vertical gradients within the canopy, or directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth System model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (c. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1 : 1 comparison (slope increases from c. 0.34 to 0.91 for the gymnosperm forest, and from c. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth System modeling in general.

Invasive fungal infections are gaining increasing importance in intensive care medicine. The aim of this article is to present an update on recent developments in the field of invasive fungal infection in critically ill patients. Particular emphasis is placed on the recently described invasive mold infections in patients with acute respiratory distress syndrome due to influenza or COVID-19. Detecting high-risk patients and the optimal diagnostic and therapeutic strategies play a decisive role to improve outcome. Invasive Mykosen spielen in der Intensivmedizin eine zunehmende Rolle. Das Erkennen von Risikopatienten und das richtige Diagnose- und Therapiemanagement spielen dabei die entscheidende Rolle. INFLUENZA- UND COVID-19-ASSOZIIERTE PULMONALE ASPERGILLOSE: Neben der invasiven Candidiasis wurden zuletzt vermehrt Superinfektionen durch Schimmelpilze im Rahmen von schweren Influenza- und COVID-19-Infektionen beobachtet. Diese treten ohne die „klassischen“ Risikofaktoren für invasive Aspergillosen auf und bedürfen daher der besonderen Aufmerksamkeit der Intensivmediziner. Die Superinfektion mit Aspergillus-Spezies bei Influenza ist insofern neu, da bislang invasive Aspergillosen nur bei immunsupprimierten Patienten beschrieben wurden. Auch bei SARS-CoV-2 fanden sich schnell Berichte von COVID-19-assoziierten pulmonalen Aspergillosen, die möglicherweise zu einer Erhöhung der Mortalität führen können.

Dry Mediterranean forests are characterized by a short rainy season followed by a long dry period with high temperatures and radiation levels. These ecosystem are also exposed to large interannual variations in precipitation. Taking advantage of contrasting rainfall years, we investigated the opportunistic nature of pine trees in this region. In our study site (the Yatir forest) mean annual precipitation is 288 mm, but it was 220 and 420 mm in the hydrological years 2018/19 and 2019/20, respectively. We used fluorescence measurements at the leaf, tower, and satellite scales, together with reflectance indices and eddy covariance measurements to assess the physiological response in the dry stressful season in these contrasting years.The results showed that following a low rainfall season, soil moisture contes (SWC) reaches the 16% threshold of no traspirable water in spring, followed by larg decrease in carbon uptake and quantum yield of photosynthesis and the activation of protection mechanisms, such as decrease in chlorophyll content, large NPQ, and drop in the chlorophyll to cartenoid ratio (CCI index, obtained from canopy reflectance). Following the high rainfall year, the active season is extended (as indicated also by the satellite data), but even after the SWC threshold is reached, and mid-day VPD reaches ~5 KPa), carbon uptake continues, the amount of energy allocated to photochemistry remains high (high Fv/Fm and Y(II) levels), without the onset of protective mechanisms: No decrease in leaf chlorophyll and in NPQ, or decrease in CCI. We hypothesize that the opportunistic response of the dry-land forest must rely on yet unidentified water storage outside the root zone (e.g. deep soil pockets within the bedrock, or within the plants), which allow the plant to maintain high, with the only apparent adjustment reflected in shift of activity to early morning hours, when VPD is still low but the PAR levels are sufficiently high.