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The ligand-activated nuclear receptor peroxisome-proliferator-activated receptor-γ (PPARG or PPARγ) represents a potential target for a new generation of cancer therapeutics, especially in muscle-invasive luminal bladder cancer where PPARγ is a critical lineage driver. Here we disclose the discovery of a series of chloro-nitro-arene covalent inverse-agonists of PPARγ that exploit a benzoxazole core to improve interactions with corepressors NCOR1 and NCOR2.

Machine learning algorithms have become widely used for geospatial applications, including spatial mapping and upscaling ecological variables and traits. Multivariate splines, random forests, and neural networks have been widely used to upscale a few sparse measurements to larger areas. Machine learning models, however, cannot offer reliable predictions in out-of-the-sample areas, which is often the case in such applications [1,2]. In [3], an area of applicability is proposed as an extrapolation index based on the minimum distance to the training data in the multidimensional predictor space with predictors being weighted by their respective importance in the model. We propose Gaussian Processes (GPs) to derive such extrapolation indicator [4]. A GP is a popular method in machine learning and multivariate statistics for regression problems. It provides a probabilistic description of the predictive function, so one can derive both predictive mean and variance for the predictions on new data. We here suggest using the predictive variance as an indicator for extrapolation and show the relation with a customized dissimilarity index computed that follows the Area of Applicability methodology proposed in [3]. We show the relation and in some cases the generalization in a set of controlled synthetic experiments and for vegetation traits global mapping using remote sensing, meteorological variables and the (huge yet sparse and biased) TRY database. This relation opens the door to a more sound way of identifying and characterizing extrapolation regimes through GPs in geospatial and upscaling applications.References[1] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein (Editors). Wiley & Sons 2021[2] Perspective on Deep Learning for Earth Sciences. Camps-Valls, Gustau. Generalization with Deep Learning: for Improvement on Sensing Capability, World Scientific Pub Co Inc 2021[3] Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620–1633. https://doi.org/10.1111/2041-210X.13650[4] A Survey on Gaussian Processes for Earth Observation Data Analysis: A Comprehensive Investigation. Camps-Valls, G. and Verrelst, J. and Muñoz-Marí, J. and Laparra, V. and Mateo-Jiménez, F. and Gómez-Dans, J. IEEE Geoscience and Remote Sensing Magazine 2

The McMurdo Dry Valleys (MDVs) in Antarctica have a unique environment classified as a hyper-arid desert with glacier runoff being the main source of liquid water. Previous studies have identified winds as the controlling factor of the climate in this region and especially the occurrence of foehn induced warming. Episodic foehn warming during the austral summer can contribute to above freezing temperatures sustained for multiple days. Years with extreme glacial runoff leading to flooding have been correlated with a higher occurrence of foehn induced warming events. Understanding the temporal availability of meltwater caused by extreme meteorological events is highly important since it is a dependant variable to the functioning of the area’s fragile ecosystem. Synoptic scale circulations in the surrounding Ross Sea Region are a driving factor for the occurrence of foehn warming in the MDVs with the local mesoscale meteorology modulating the spatiotemporal variability of the foehn-induced near-surface warming. AntAir ICE, a newly developed daily mean near surface air temperature dataset with a spatial grid resolution of 1 km2 has proven capable of capturing these mesoscale temperature variabilities for multiple seasons within the complex topography of the MDVs. A case study on the 2nd of January 2020 where the maximum temperature measured in a Lake Vanda automatic weather station was above +9 degrees Celsius with multiple valleys experiencing foehn induced warming, displayed a clear warming signal for the MDVs in AntAir ICE. The atmospheric dynamic analysis from the numerical weather prediction model the Antarctic Mesoscale Prediction System (AMPS) indicated a clear foehn signature. This event was linked to a meso-low located in the Ross Sea which was detected in the climate re-analysis ERA5 mean sea level pressure dataset. By confidently identifying these warming events within the MDVs where there is a relatively high availability of Automatic Weather Stations and AMPS predictions, has allowed for further exploration of extreme sustained warming and potentially foehn induced warming along the terrestrial coastal margin of Antarctica. Using AntAir ICE, warming events during the austral summer season from November to February for the period 2003 to 2021 with sustained daily mean temperatures above freezing for multiple days have been identified for the Ross Sea Region. This study aims at capturing the mesoscale meteorological and climatological variability for multiple seasons within the Ross Sea Region, while linking these extreme warming events to larger scale circulation patterns can allow for understanding local extreme events in context of shifting large scale circulation drivers.

Representing the Earth’s most efficient terrestrial carbon store, intact peatlands play a key role in climate change mitigation strategies and provide multiple other ecosystem services such as flood prevention and refugia for rare species. The carbon sink function of peatlands is yet highly dependent on water saturation and vegetation composition. Nevertheless, drainage and peat extraction during the past centuries until today led to intense peatland degradation and turned more than half of all European peat soils and more than 90 percent of peat soils in Germany into a carbon source. Efforts have been increasingly made since the 1990s to restore peatlands, mainly by rewetting to recover peatland typical hydrological conditions. However, there is a lack of knowledge on restoration success for numerous sites, due to difficulties in funding long-term hydrological monitoring. Satellite remote sensing is an excellent method to address this deficiency, as it provides spatially continuous and temporally highly resolved information on the environment, including peatlands.Making use of freely available data of the Landsat Mission, this study aims to analyze trends in surface moisture conditions of European peatlands over the last decades, a time frame in which many restoration measures have been implemented. We performed a pixel-wise trend analysis for European peatlands using the Normalized Difference Moisture Index as moisture indicator based on image time-series reaching back to 1984 and a spatial resolution of 30 x 30 m. Trend statistics using Mann-Kendall’s tau and Sen’s slope were calculated for each month separately to also enable analysis of changes in specific seasons, such as the growing season or shoulder seasons important for water recharge of the sites. Based on a random sample of peatland sites across all Europe, we show first results of european-wide trend patterns. For small-scale visualization and to facilitate a spatially explicit long-term monitoring of peatlands in active restoration management, we further present an open-source Google-Earth-Engine (GEE) application which additionally provides insights into changes in vegetation, as represented by the Normalized Difference Vegetation Index. Besides allowing the interpretation of changes in surface moisture conditions over the past decades, the GEE tool can also be used in the future to assess potential restoration sites or to improve our understanding concerning the resilience of peatlands in scenarios of a warming climate, where research is still in its infancy. The continental coverage of the analysis in combination with a temporal coverage of several decades on a monthly resolution offers exceptional possibilities for spatial planning and evaluation of European peatland restoration and can therefore contribute to a cost-effective implementation of climate change mitigation measures.

Soil temperature and soil moisture variations have large effects on ecological processes in the soil. To investigate and understand these processes, high-resolution data of soil temperature and soil moisture are required. Here, we present an approach to generate data of soil temperature and soil moisture continuously in space, depth, and time for a 400 km² study area in the Fichtel Mountains (Germany). As reference data, measurements with 1 m long soil probes were taken. To cover many different locations, the available 15 soil probes were shifted regularly in the course of one year. With this approach, around 250 different locations in forest sites, on meadows and on agricultural fields were captured under a variety of meteorological conditions. These measurements are combined with readily available meteorological data, satellite data and soil maps in a machine learning approach to learn the complex relations between these variables. We aim for a model which can predict the soil temperature and soil moisture continuously for our study area in the Fichtel Mountains, with a spatial resolution of 10 m x 10 m, down to 1 m depth with segments of 10 cm each and in an hourly resolution in time. Here, we present the results of our pilot study where we focus on the temperature and moisture change within the depth down to 1 m at one single location. To take temporal lags into account, we construct a Long Short-Term Memory network based on meteorological data as predictors to make temperature and moisture predictions in time and depth. The results indicate a high ability of the model to reproduce the time series of the single location and highlight the potential of the approach for the space-time-depth mapping of soil temperature and soil moisture.

Bioorganic & medicinal chemistry 78, 117130 (2023). doi:10.1016/j.bmc.2022.117130, PPAR gamma (PPARG) is a ligand activated transcription factor that regulates genes involved in inflammation, bone biology, lipid homeostasis, as well as a master regulator of adipogenesis and a potential lineage driver of luminal bladder cancer. While PPARG agonists lead to transcriptional activation of canonical target genes, inverse agonists have the opposite effect through inducing a transcriptionally repressive complex leading to repression of canonical target gene expression. While many agonists have been described and tested clinically, inverse agonists offer an underexplored avenue to modulate PPARG biology in vivo. Current inverse agonists lack favorable in vivo properties; herein we describe the discovery and characterization of a series of orally bioavailable 4-chloro-6-fluoroisophthalamides as covalent PPARG inverse-agonists, BAY-5516, BAY-5094, and BAY-9683. Structural studies of this series revealed distinct pre- and post-covalent binding positions, which led to the hypothesis that interactions in the pre-covalent conformation are primarily responsible for driving affinity, while interactions in the post-covalent conformation are more responsible for cellular functional effects by enhancing PPARG interactions with its corepressors. The need to simultaneously optimize for two distinct states may partially explain the steep SAR observed. Exquisite selectivity was achieved over related nuclear receptors in the subfamily due in part to a covalent warhead with low reactivity through an SNAr mechanism in addition to the specificity gained through covalent binding to a reactive cysteine uniquely positioned within the PPARG LBD. BAY-5516, BAY-5094, and BAY-9683 lead to pharmacodynamic regulation of PPARG target gene expression in vivo comparable to known inverse agonist SR10221 and represent new tools for future in vivo studies to explore their potential utility for treatment of disorders of hyperactivated PPARG including luminal bladder cancer and other disorders., Published by Elsevier, Amsterdam [u.a.]

Zusammenfassung Hintergrund Aufmerksamkeitsdefizit‑/Hyperaktivitätsstörung (ADHS) oder oppositionelle Verhaltensstörung (OPP) gehen mit unruhigem, oppositionellem und aggressivem Verhalten, negativen Interaktionen zwischen Kind und Eltern und langfristig häufig mit belasteten Eltern-Kind-Beziehungen einher. Kognitiv-behaviorale Elterntrainings, die für die Behandlung externaler Störungen als evidenzbasiert gelten, versuchen problematische situative und Verstärkerbedingungen sowie negative Interaktionen zwischen Eltern und Kindern zu verändern. Ziel Es sollen Online-Selbsthilfetrainings für Eltern von Kindern mit externalisierenden Verhaltensproblemen entwickelt werden. Methode Bestehende Face-to-face-Elterntrainings wurden in Online-Formate überführt und inhaltlich erweitert. Eine Wirksamkeitsuntersuchung erfolgt im Rahmen von randomisierten Kontrollstudien. Ergebnisse Der ADHS-Elterntrainer richtet sich an Eltern von Kindern mit subklinischen sowie klinisch relevanten ADHS-Symptomen sowie ggf. komorbid weiteren externalen Symptomen. Er zielt darauf ab, Störungsverständnis aufzubauen, die elterliche Steuerung zu erhöhen und die Eltern-Kind-Beziehung zu verbessern. Bei Kindern mit Emotionsregulationsstörungen, sog. affektiver Dysregulation, ist darüber hinaus die elterliche Unterstützung beim Erwerb funktionaler Emotionsregulationsstrategien nötig. Für das ADOPT-Online-Elterntraining (affektive Dysregulation – Optimierung von Prävention und Therapie), das sich an Eltern von Kindern mit affektiver Dysregulation richtet, wurde das Konzept des ADHS-Elterntrainers um Interventionen zum Umgang mit negativen Gefühlen des Kindes und der Eltern erweitert. Diskussion Bei entsprechend positiven Effekten auf die kindliche Symptomatik innerhalb der Evaluationsstudien könnte das ADOPT-Online-Elterntraining wie bereits beim ADHS-Elterntrainer geschehen im präventiven oder therapeutischen Setting eingesetzt werden.

Global-scale maps are an important tool to provide ecologically relevant environmental variables to researchers and decision makers. Usually, these maps are created by training a machine learning algorithm on field-sampled reference data and the application of the resulting model to associated information from satellite imagery or globally available environmental predictors. However, field samples are often sparse and clustered in geographic space, representing only parts of the global environment. Machine learning models are therefore prone to overfit to the specific environments they are trained on - especially when a large set of predictor variables is utilized. Consequently, model validations have to include an analysis of the models transferability to regions where no training samples are available e.g. by computing the Area of Applicability (AOA, Meyer and Pebesma 2021) of the prediction models.Here we reproduce three recently published global environmental maps (soil nematode abundances, potential tree cover and specific leaf area) and assess their AOA. We then present a workflow to increase the AOA (i.e. transferability) of the machine learning models. The workflow utilizes spatial variable selection in order to train generalized models which include only predictors that are most suitable for predictions in regions without training samples. We compared the results to the three original studies in terms of prediction performance and AOA. Results indicate that reducing predictors to those relevant for spatial prediction, leads to a significant increase of model transferability without significant decrease of the prediction quality in areas with high sampling density.Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution 2041–210X.13650 (2021) doi:10.1111/2041-210X.13650.

Context Piospheres describe herbivore utilization gradients around watering points, as commonly found in grass-dominated ecosystems. Spatially explicit, dynamic models are ideal tools to study the ecological and economic problems associated with the resulting land degradation. However, there is a need for appropriate landscape input maps to these models that depict plausible initial vegetation patterns under a range of scenarios. Objectives Our goal was to develop a spatially-explicit piosphere landscape generator (PioLaG) for semi-arid savanna rangelands with a focus on realistic vegetation zones and spatial patterns of basic plant functional types around livestock watering points. Methods We applied a hybrid modelling approach combining aspects of both process- and pattern-based modelling. Exemplary parameterization of PioLaG was based on literature data and expert interviews in reference to Kalahari savannas. PioLaG outputs were compared with piosphere formations identified on aerial images. Results PioLaG allowed to create rangeland landscapes with piospheres that can be positioned within flexible arrangements of grazing units (camps). The livestock utilization gradients showed distinct vegetation patterns around watering points, which varied according to the pre-set initial rangeland condition, grazing regime and management type. The spatial characteristics and zoning of woody and herbaceous vegetation were comparable to real piosphere patterns. Conclusions PioLaG can provide important input data for spatial rangeland models that simulate site-specific savanna dynamics. The created landscapes can also be used as a direct decision support for land managers in attempts to maintain or restore landscape functionality and key ecosystem services such as forage production.

1. Several spatial and non-spatial Cross-Validation (CV) methods have been used to perform map validation when additional sampling for validation purposes is not possible, yet it is unclear in which situations one CV method might be preferred over the other. Three factors have been identified as determinants of the performance of CV methods for map validation: the prediction area (geographical interpolation vs. extrapolation), the sampling pattern and the landscape spatial autocorrelation. In this study, we propose a new CV strategy that takes the geographical prediction space into account, and test how the new method compares with other established CV methods under different configurations of these three factors. 2. We propose a variation of Leave-One-Out (LOO) CV for map validation, called Nearest Neighbour Distance Matching (NNDM) LOO CV, in which the nearest neighbour distance distribution function between the test and training data during the CV process is matched to the nearest neighbour distance distribution function between the target prediction and training points. Using random forest as a machine learning algorithm, we then examine the suitability of NNDM LOO CV as well as the established LOO (non-spatial) and buffered-LOO (bLOO, spatial) CV methods in two simulations with varying prediction areas, landscape autocorrelation and sampling distributions. 3. LOO CV provided good map accuracy estimates in landscapes with short autocorrelation ranges, or when estimating geographical interpolation map accuracy with randomly distributed samples. bLOO CV yielded realistic error estimates when estimating map accuracy in new prediction areas, but generally overestimated geographical interpolation errors. NNDM LOO CV returned reliable estimates in all scenarios we considered. 4. While LOO and bLOO CV provided reliable map accuracy estimates only in certain situations, our newly proposed NNDM LOO CV method returned robust estimates and generalised to LOO and bLOO CV whenever these methods were the most appropriate approach. Our work recognises the necessity of considering the geographical prediction space when designing CV-based methods for map validation.

Monitoring of climate variables such as air temperature is gaining increasing importance under climate change. This study aimed at developing an hourly gridded 3.5 × 3.5 km air temperature (Tair) data set for entire South Africa. In a Random Forest approach, MSG SEVIRI data from 2010 to 2014 were used and related to Tair measured by 78 weather stations. An external validation on new climate stations and years that were not used for model training indicated the ability of the model to predict Tair with a RMSE of 2.61 °C and a R2 of 0.89. The resulting model can be applied to the entire MSG SEVIRI time series since 2004. It hence allows for spatio-temporal pattern analysis as well as for the detection of trends which is relevant in the context of climate change.

Bush encroachment is a highly relevant environmental issue in South African savannas, influencing ecological processes as well as the grazing capacity of the land. However the drivers of bush encroachment are not yet fully revealed which can partly be attributed to the problem that large-scale data of woody vegetation cover are missing. Using a multi-scale and a multi-sensor approach, this study aimed at providing the status of woody vegetation cover for the Molopo Area in South Africa. Training data for woody vegetation was derived from unsupervised classification of high-resolution aerial image tiles. To derive spatially continuous estimates of fractional woody cover for the entire Molopo Area, Sentinel-1 and Sentinel-2 data were applied in a machine learning based modelling approach. Therefore, a database of training samples was generated by aggregating the classified aerial image tiles to the geometry of the Sentinel data. A Random Forest algorithm with a forward feature variable selection was then trained to relate the spectral and radar information to fractional woody cover. The model was applied to make spatial predictions of fractional woody cover at 10 m resolution for the entire Molopo Area for the year 2016. Spatial cross-validation revealed a prediction error in fractional cover of 12%. The derived model and cover data show the potential for upcoming time series analysis of Sentinel-based woody cover estimates which can serve as a basis to bring new insights into the drivers of bush encroachment.

Expansive Verhaltensstorungen stellen ein haufiges Problem dar und gehen mit erheblichen Beeintrachtigungen und hohen gesellschaftlichen Kosten einher. Durch die Einfuhrung eines mehrstufigen und multimodalen Konzepts, welches evidenzbasierte Masnahmen an die individuellen Bedarfe der jeweiligen Schulerinnen und Schuler (SuS) anpasst, konnen Schulen zur Pravention von expansivem Verhalten beitragen. Der Ansatz des „schoolwide positive behavior supports“ (SW-PBS) stellt ein solches Praventionskonzept dar. Im vorliegenden Beitrag stellen wir die konkrete Konzeption des SW-PBS zur Pravention expansiven Problemverhaltens fur die Grundschule sowie das begleitende Evaluationskonzept vor.

Fire events on a landscape scale are a widespread global phenomenon that influences the interactions between atmosphere and biosphere. Global burned area (BA) products derived from satellite images are used in dynamic vegetation fire modules to estimate greenhouse gas emissions, available fuel biomass and anthropic factors driving fire spread. Fire size and shape complexity from individual fire events can provide better estimates of fuel consumption, fire intensity, post fire vegetation recovery and their effects on landscape changes to better understand regional fire dynamics. Especially in the Brazilian savannas (Cerrado), a mosaic of heterogeneous vegetation where has prevailed an official “zero-fire” policy for decades leading to an increase in large wildfires, intensified also by rapid changes of land use using fire to land clearing in agriculture and livestock purposes. In this way, we aim to assess the fire size and shape patterns in Cerrado from 2013 to 2015, identifying each fire patch event from Landsat BA product and calculating its fire features with landscape metrics. We calculated its surface area to evaluate fire size and the metrics of shape index, core area and eccentricity from an ellipse fitting from burned pixels to estimate the fire shape complexity. The study focused on 48 Landsat path/row scenes and the analysis final compared the fire features of overlapped patches between the years. The total number of coincident fire patches is higher between the years 2013 and 2015 than 2013-2014 and 2014-2015. Large fires are found in the north and east regions for all comparisons. In this region, high core area values are consistent for having large areas of burnt patches and low shape index values and more elongated patches revealed a low fire shape complexity. These results demonstrate a greater burned area in the north, where the remaining native vegetation and less fragmented landscapes allow the fire to spread, when associated with favorable meteorological conditions. However, with the implementation of a new agricultural frontier in 2015, this region is under greater anthropic pressure with positive trends to land use. In the south, the fire shapes are already more complex and smaller because they are from agricultural areas historically developed, and consequently the landscape is more fragmented. Our results demonstrate a distinct spatial pattern of fire shape and size in Cerrado related to fragmentation of landscape and fire use to land cleaning. This information can help the modelling estimates of fire spread processes driven by topography, orientation of watersheds or dominant winds at local level, contributing to understanding the feedback with land cover/use, climate and biophysical characteristics at regional level to develop strategies for fire management.Acknowledges: J.N is funded by the 'Women in Research'-fellowship program (WWU Münster) and within the context of BIOBRAS Project “Research-based learning in neglected biodiverse ecosystems of Brazil”; funding by DAAD (number 57393735); validation dataset was performed under the Andurá project (number 441971/2018–0) funding by CNPq

Predictive modelling using machine learning has become very popular for spatial mapping of the environment. Models are often applied to make predictions far beyond sampling locations where new geographic locations might considerably differ from the training data in their environmental properties. However, areas in the predictor space without support of training data are problematic. Since the model has no knowledge about these environments, predictions have to be considered uncertain. Estimating the area to which a prediction model can be reliably applied is required. Here, we suggest a methodology that delineates the "area of applicability" (AOA) that we define as the area, for which the cross-validation error of the model applies. We first propose a "dissimilarity index" (DI) that is based on the minimum distance to the training data in the predictor space, with predictors being weighted by their respective importance in the model. The AOA is then derived by applying a threshold based on the DI of the training data where the DI is calculated with respect to the cross-validation strategy used for model training. We test for the ideal threshold by using simulated data and compare the prediction error within the AOA with the cross-validation error of the model. We illustrate the approach using a simulated case study. Our simulation study suggests a threshold on DI to define the AOA at the .95 quantile of the DI in the training data. Using this threshold, the prediction error within the AOA is comparable to the cross-validation RMSE of the model, while the cross-validation error does not apply outside the AOA. This applies to models being trained with randomly distributed training data, as well as when training data are clustered in space and where spatial cross-validation is applied. We suggest to report the AOA alongside predictions, complementary to validation measures., 16 pages, 10 figures, to be submitted to Methods in Ecology and Evolution

Summary Structural mutants of p53 induce global p53 protein destabilization and misfolding, followed by p53 protein aggregation. First evidence indicates that p53 can be part of protein condensates and that p53 aggregation potentially transitions through a condensate-like state. We show condensate-like states of fluorescently labeled structural mutant p53 in the nucleus of living cancer cells. We furthermore identified small molecule compounds that interact with the p53 protein and lead to dissolution of p53 structural mutant condensates. The same compounds lead to condensation of a fluorescently tagged p53 DNA-binding mutant, indicating that the identified compounds differentially alter p53 condensation behavior depending on the type of p53 mutation. In contrast to p53 aggregation inhibitors, these compounds are active on p53 condensates and do not lead to mutant p53 reactivation. Taken together our study provides evidence for structural mutant p53 condensation in living cells and tools to modulate this process., Graphical Abstract, Highlights • Fluorescent versions of structural p53 mutants form protein condensates in cells • We report the identification of p53-interacting small molecules • Identified compounds differentially alter fluorescent mutant p53 condensation behavior, Biochemistry Methods; Medical Biochemistry; Structural Biology

Anthropogenic Climate Change is expected to take a toll on the Antarctic environment and its biodiversity, which is concentrated on the continent’s few ice-free areas, such as the McMurdo Dry Valleys (MDV). To model the current terrestrial habitat distribution and predict possible climate induced changes, high spatial and temporal resolution abiotic variables, especially land surface temperature (LST) and soil moisture are needed, but are currently unavailable.The aim of this project is to fill this gap and create a high resolution LST dataset of the Antarctic Dry Valleys. This variable is acquired in a high temporal resolution (sub-daily) by the MODIS sensor aboard Terra and Aqua satellites. However, as LST varies greatly in space, the spatial resolution provided by this data source (1000 m) is too low to give a meaningful impression of LST and to study biodiversity patterns. Therefore, we use data from Landsat and ASTER sensors as a reference to downscale MODIS LST to a spatial resolution of 30 m. 7 year’s worth of satellite data as well as terrain-derived auxiliary variables went into the development of the model, which predicts 30 m LST for the Antarctic Dry Valleys. To model complex relations between terrain, radiation, land cover and LST, machine learning models are used. Multiple algorithms (Random Forest, NN, SVM, Gradient Boosting) are compared to find the best approach for predicting high resolution LST based on MODIS data. Using the best performing model, a daily dataset is created that provides LST for the Antarctic Dry Valleys from 2002 on.

Spatial mapping is an important task in environmental science to reveal spatial patterns and changes of the environment. In this context predictive modelling using flexible machine learning algorithms has become very popular. However, looking at the diversity of modelled (global) maps of environmental variables, there might be increasingly the impression that machine learning is a magic tool to map everything. Recently, the reliability of such maps have been increasingly questioned, calling for a reliable quantification of uncertainties.Though spatial (cross-)validation allows giving a general error estimate for the predictions, models are usually applied to make predictions for a much larger area or might even be transferred to make predictions for an area where they were not trained on. But by making predictions on heterogeneous landscapes, there will be areas that feature environmental properties that have not been observed in the training data and hence not learned by the algorithm. This is problematic as most machine learning algorithms are weak in extrapolations and can only make reliable predictions for environments with conditions the model has knowledge about. Hence predictions for environmental conditions that differ significantly from the training data have to be considered as uncertain.To approach this problem, we suggest a measure of uncertainty that allows identifying locations where predictions should be regarded with care. The proposed uncertainty measure is based on distances to the training data in the multidimensional predictor variable space. However, distances are not equally relevant within the feature space but some variables are more important than others in the machine learning model and hence are mainly responsible for prediction patterns. Therefore, we weight the distances by the model-derived importance of the predictors. As a case study we use a simulated area-wide response variable for Europe, bio-climatic variables as predictors, as well as simulated field samples. Random Forest is applied as algorithm to predict the simulated response. The model is then used to make predictions for entire Europe. We then calculate the corresponding uncertainty and compare it to the area-wide true prediction error. The results show that the uncertainty map reflects the patterns in the true error very well and considerably outperforms ensemble-based standard deviations of predictions as indicator for uncertainty.The resulting map of uncertainty gives valuable insights into spatial patterns of prediction uncertainty which is important when the predictions are used as a baseline for decision making or subsequent environmental modelling. Hence, we suggest that a map of distance-based uncertainty should be given in addition to prediction maps.

Das hier vorgestellte Kapitel beinhaltet eine Konzeptualisierung der Begriffe Empathie und Mitgefuhl. Im Fokus steht die Bedeutung von Empathie und Mitgefuhl im therapeutischen Kontext, die besonders im Konzept des empathischen Verstehens der personzentrierten Psychotherapie nach Rogers verwurzelt ist und neben der Haltung des Therapeuten und der Beziehungsgestaltung auch Gesprachsfuhrungstechniken wie das Verbalisieren emotionaler Erlebnisinhalte (VEE) umfasst. Daruber hinaus werden Ansatzpunkte fur eine explizite Forderung von Empathie und Mitgefuhl im therapeutischen und praventiven Setting beschrieben.

Air temperature is an important baseline parameter for terrestrial Antarctica in the context of patterns and processes in climatology, hydrology or ecology. There are still large uncertainties on how the Antarctic system responds to spatio-temporal variability of temperature. This can partly be attributed to the lack of high resolution datasets. In this paper, we present AntAir, a new dataset of gridded air temperatures in 1 km spatial and daily temporal resolution that is available since 2003. AntAir was created by modelling daily air temperature from MODIS land surface temperature using machine learning algorithms. Data from 70 weather stations was used as a reference. Daily temperatures could be estimated with a R2 of 0.91 and a RMSE of 5.07 °C validated on independent years. The performance to estimate the time series of a new spatial location was R2 = 0.78 and RMSE = 5.83 °C. Hence the high spatial and temporal resolution of the dataset as well as the high accuracy make AntAir an important baseline dataset for a wide range of applications in environmental science of Antarctica. The dataset is available at https://doi.pangaea.de/10.1594/PANGAEA.902166 (daily, Meyer et al., 2019a) and https://doi.pangaea.de/10.1594/PANGAEA.902193 (monthly, Meyer et al., 2019b).

Spatial patterns of soil respiration (SR) and its sensitivity to temperature (Q10) are one of the key uncertainties in climate change research but since their assessment is very time-consuming, large data sets can still not be provided. Here, we investigated the potential of mid-infrared spectroscopy (MIRS) to predict SR and Q10 values for 124 soil samples of diverse land use types taken from a 2868 km2 catchment (Rur catchment, Germany/Belgium/Netherlands). Soil respiration at standardized temperature (25 °C) and soil moisture (45% of maximum water holding capacity, WHC) was successfully predicted by MIRS coupled with partial least square regression (PLSR, R2 = 0.83). Also the Q10 value was predictable by MIRS-PLSR for a grassland submodel (R2 = 0.75) and a cropland submodel (R2 = 0.72) but not for forested sites (R2 = 0.03). In order to provide soil respiration estimates for arbitrary conditions of temperature and soil moisture, more flexible models are required that can handle nonlinear and interacting relations. Therefore, we applied a Random Forest model, which includes the MIRS spectra, temperature, soil moisture, and land use as predictor variables. We could show that SR can be simultaneously predicted for any temperature (5–25 °C) and soil moisture level (30–75% of WHC), indicated by a high R2 of 0.73. We conclude that the combination of MIRS with sophisticated statistical prediction tools allows for a novel, rapid acquisition of SR and Q10 values across landscapes and thus to fill an important data gap in the validation of large scale carbon modeling.

Importance of target-oriented validation strategies for spatio-temporal prediction models is illustrated using two case studies: (1) modelling of air temperature ( T a i r ) in Antarctica, and (2) modelling of volumetric water content (VW) for the R.J. Cook Agronomy Farm, USA. Performance of a random k-fold cross-validation (CV) was compared to three target-oriented strategies: Leave-Location-Out (LLO), Leave-Time-Out (LTO), and Leave-Location-and-Time-Out (LLTO) CV. Results indicate that considerable differences between random k-fold ( R 2 = 0.9 for T a i r and 0.92 for VW) and target-oriented CV (LLO R 2 = 0.24 for T a i r and 0.49 for VW) exist, highlighting the need for target-oriented validation to avoid an overoptimistic view on models. Differences between random k-fold and target-oriented CV indicate spatial over-fitting caused by misleading variables. To decrease over-fitting, a forward feature selection in conjunction with target-oriented CV is proposed. It decreased over-fitting and simultaneously improved target-oriented performances (LLO CV R 2 = 0.47 for T a i r and 0.55 for VW).

In mountain geosystems, rockfalls are among the most effective sediment transfer processes, reflected in the regional-scale distribution of talus slopes. However, the understanding of the key controlling factors seems to decrease with increasing spatial scale, due to emergent and complex system behavior and not least to recent methodological shortcomings in rockfall modeling research. In this study, we aim (i) to develop a new approach to identify major regional-scale rockfall controls and (ii) to quantify the relative importance of these controls. Using a talus slope inventory in the Turtmann Valley (Swiss Alps), we applied for the first time the decision-tree based random forest algorithm (RF) in combination with a principal component logistic regression (PCLR) to evaluate the spatial distribution of rockfall activity. This study presents new insights into the discussion on whether periglacial rockfall events are controlled more by topo-climatic, cryospheric, paraglacial or/and rock mechanical properties. (i) Both models explain the spatial rockfall pattern very well, given the high areas under the Receiver Operating Characteristic (ROC) curves of > 0.83. Highest accuracy was obtained by the RF, correctly predicting 88% of the rockfall source areas. The RF appears to have a great potential in geomorphic research involving multicollinear data. (ii) The regional permafrost distribution, coupled to the bedrock curvature and valley topography, was detected to be the primary rockfall control. Rockfall source areas cluster within a low-radiation elevation belt (2900–3300 m a.s.l,) consistent with a permafrost probability of > 90%. The second most important factor is the time since deglaciation, reflected by the high abundance of rockfalls along recently deglaciated ( The study demonstrates the benefit of combined statistical approaches for predicting rockfall activity in deglaciated, permafrost-affected mountain valleys and highlights the complex interplay between rock mechanical, paraglacial and topo-climatic controls at the regional scale.

Estimating rainfall areas and rates from geostationary satellite images has the opportunity of both, a high spatial and a high temporal resolution which cannot be achieved by other satellite-based systems until now. Most recent retrieval techniques are solely based on spectral channels of the satellites. These retrievals can be classified as ‘purely pixel-based’ because no information about the neighbourhood pixels is included. Assuming that precipitation is highly correlated with cloud processes and therefore with cloud texture, textural information derived from the neighbourhood of a pixel might give valuable information about the cloud type and hence about a respective probability of the rainfall rate. To study the potential of textural variables to improve optical rainfall retrieval techniques, rainfall areas and rainfall rates were estimated over Germany for the year 2010 using a neural network approach. In addition to the spectral predictor variables from Meteosat Second Generation (MSG), different Grey Level Co-occurance Matrix (GLCM) based textural variables were calculated from all MSG channels. Using recursive feature selection, models were trained and their performance was compared to spectral-only models. Contrary to the expectations, the performance of the models did not increase when textural information was included.

MTH1 is a hydrolase responsible for sanitization of oxidized purine nucleoside triphosphates to prevent their incorporation into replicating DNA. Early tool compounds published in the literature inhibited the enzymatic activity of MTH1 and subsequently induced cancer cell death; however recent studies have questioned the reported link between these two events. Therefore, it is important to validate MTH1 as a cancer dependency with high quality chemical probes. Here, we present BAY-707, a substrate-competitive, highly potent and selective inhibitor of MTH1, chemically distinct compared to those previously published. Despite superior cellular target engagement and pharmacokinetic properties, inhibition of MTH1 with BAY-707 resulted in a clear lack of in vitro or in vivo anticancer efficacy either in mono- or in combination therapies. Therefore, we conclude that MTH1 is dispensable for cancer cell survival.

Acute myeloid leukemia (AML), the most common acute leukemia in adults, is an aggressive hematologic malignancy resulting in bone marrow failure with a poor outcome; overall survival is approximately 25% at five years. Treatment options, in particular for the elderly population, are limited. Induction chemotherapy of cytarabine and an anthracycline (7+3) remains unchanged standard of care since its introduction in the early 1970s and there is a high medical need for new therapies (Yates et al. Cancer Chemother Rep 1973). DHODH is a key enzyme in the de novo pyrimidine synthesis converting dihydroorotate to orotate. Using a HOXA9 driven phenotypic screen to overcome differentiation arrest in myeloid cells, we have recently identified DHODH as a surprising novel target to overcome differentiation blockade in AML (Sykes et al. Cell 2016). Differentiation therapy already showed its enormous clinical benefit potential in the small subset of patients diagnosed with acute promyelocytic leukemia (APL) following treatment with all-trans retinoic acid with five-year survival exceeding 85% and should be considered the ultimate therapeutic goal for all AML subsets (Lo-Coco et al. NEJM 2013). Here, we disclose for the first time the structure and functional characterization of the novel DHODH inhibitor BAY 2402234. BAY 2402234 is a selective low-nanomolar inhibitor of human DHODH enzymatic activity. In vitro, it potently inhibits proliferation of AML cell lines in the sub-nanomolar to low-nanomolar range. BAY 2402234 induces differentiation of AML cell lines also in a sub-nanomolar to low-nanomolar range, demonstrating the anticipated mode of action in cellular mechanistic assays. In vivo, BAY 2402234 exhibits strong in vivo anti-tumor efficacy in monotherapy in several subcutaneous and disseminated AML xenografts as well as AML patient-derived xenograft (PDX) models. Target engagement of the novel DHODH inhibitor BAY 2402234 can be observed by increase of tumoral and plasma dihydroorotate levels after treatment with the inhibitor. Consistent with the in vitro data BAY 2402234 induces AML differentiation in vivo as detected by upregulation of differentiation cell surface markers in xenograft and PDX models after treatment with the inhibitor. Furthermore, differentiation-associated transcriptomic changes were evident following a single administration of BAY 2402234 in vivo. The start of clinical investigations of BAY 2402234 is planned for early 2018. Citation Format: Andreas Janzer, Stefan Gradl, Sven Christian, Katja Zimmermann, Claudia Merz, Hanna Meyer, Timo Stellfeld, Judith Guenther, Detlef Stoeckigt, Henrik Seidel, Pascale Lejeune, Michael Bruening, Ashley Eheim, Thomas Mueller, Ralf Lesche, Martin Michels, Andrea Haegebarth, Marcus Bauser, Sherif El Sheikh, Steven Ferrara, David Sykes, David Scadden. BAY 2402234: A novel, selective dihydroorotate dehydrogenase (DHODH) inhibitor for the treatment of myeloid malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr DDT02-04.

Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions. We introduce two case studies that use remote sensing to predict land cover and the leaf area index for the "Marburg Open Forest", an open research and education site of Marburg University, Germany. We use the machine learning algorithm Random Forests to train models using non-spatial and spatial cross-validation strategies to understand how spatial variable selection affects the predictions. Our findings confirm that spatial cross-validation is essential in preventing overoptimistic model performance. We further show that highly autocorrelated predictors (such as geolocation variables, e.g. latitude, longitude) can lead to considerable overfitting and result in models that can reproduce the training data but fail in making spatial predictions. The problem becomes apparent in the visual assessment of the spatial predictions that show clear artefacts that can be traced back to a misinterpretation of the spatially autocorrelated predictors by the algorithm. Spatial variable selection could automatically detect and remove such variables that lead to overfitting, resulting in reliable spatial prediction patterns and improved statistical spatial model performance. We conclude that in addition to spatial validation, a spatial variable selection must be considered in spatial predictions of ecological data to produce reliable predictions., Comment: under review in Ecological Modelling