dataesr/scientific-paragraphs-categorization huggingface dataset:
Tumor tissue and clinical data Our cohort comprises a total of 60 patients. All available clinical data are listed in Supplementary Table  1. Where not reported, patient sex was predicted using data from the DNA methylation array. Tumor samples and clinical data were provided by multiple international collaborating centers and collected at the Department of Neuropathology of the Heidelberg University Hospital (UKHD) and at the German Cancer Research Center (DKFZ, Heidelberg, Germany) with approval from the respective institutional review boards/ethics committees and informed consent from all patients or their guardians.
DNA methylation profiling and copy number variation calling DNA methylation profiles on a genome-wide level were obtained using the Illumina Infinium HumanMethylation450 (450 k) or HumanMethylationEPIC (EPIC) BeadChip arrays according to the manufacturer's instructions (Illumina, San Diego, USA). Raw data were generated at the Genomics and Proteomics Core Facility of the DKFZ, at the Neuropathology Department of the UKHD and at respective international contributing institutes, using both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue samples. All computational analyses were performed using R (http:// www. R-proje ct. org, R Development Core Team). Using the 'conumee' R package in Bioconductor (https:// bioco nduct or. org/ packa ges/ relea se/ bioc/ html/ conum ee. html), the output data from the DNA methylation arrays were also used to investigate copy number alterations, which were assessed by manual inspection and in detail using IGV  [37, 47].
Whole exome and gene panel sequencing For n = 4 cases, whole exome sequencing (WES) data were generated for the INFORM study, from tumor and matched blood DNA, as previously described  [54]. Gene panel sequencing data were available for n = 10 cases, including 7 cases for which normal blood DNA was also sequenced. The gene panel includes 130 commonly mutated genes in CNS neoplasms and has been described previously  [39].
RNA sequencing analysis RNA sequencing data (total n = 27) for the purpose of fusion detection was either generated by contributing sites (n = 8) or locally in Heidelberg. For samples sequenced in Heidelberg, sequencing was performed from FFPE (n = 8) on the NextSeq500 (Illumina) with 75 bp paired-end reads, at the Neuropathology Department of UKHD, as previously described  [54]. For additional quantitative gene expression analysis, sequencing of high-quality RNA from frozen tissue (n = 11) was performed by the High Throughput Sequencing Unit of the Genomics and Proteomics Core Facility (GPCF) at the DKFZ using the Illumina HiSeq4000 or NovaSeq platform, with libraries prepared according to the manufacturer's instructions using the TruSeq Stranded mRNA Library Prep Kit. Gene expression data analysis was performed using the online tool 'R2: Genomics Analysis and Visualization Platform' (https:// hgser ver2.
DNA methylation profiling Routine diagnostic molecular profiling performed in Heidelberg in the context of the INFORM pipeline  [54] , the Molecular Neuropathology 2. 0 or Pediatric Targeted Therapy 2. 0 studies or otherwise in the Neuropathology Department of the UKHD  [39, 45]  revealed a small but recurring number of CNS tumors harboring fusions of the PATZ1 gene coupled to either MN1 or EWSR1 (see below). When further investigating the DNA methylation profile of these tumors within the context of a much larger reference database, they were found to form a distinct molecular cluster with several other tumors from other sources. Visualization of the genome-wide methylation pattern using t-distributed stochastic neighbor embedding (tSNE; Supplementary Fig. 1 , online resource) and uniform manifold approximation and projection (UMAP; not shown) confirmed a common pattern with broad proximity to other molecular types of both lowand high-grade glial and glioneuronal tumors.
Drug screening identifies potential candidates for future treatments Screening of cell line molecular data in the Broad Institute's Cancer Dependency Map portal (DepMap, https:// depmap. The line grows both adherently or in neurosphere conditions, and confirmatory RNA sequencing of the line validated the presence of the fusion. Although this long-term in vitro culture certainly will not recapitulate all features of the primary tumors, we nevertheless performed an initial drug screening to obtain some possible first hints at future therapeutic options. The drug screen was performed using the Hopp Children's Cancer Center, Heidelberg (KiTZ) Translational Drug Screening Unit (TDSU) library. This library comprises 74 approved cancer drugs targeting a variety of different pathways. The output chemiluminescence intensities were fed into the web-based BREEZE drug screen analysis pipeline (https:// breeze.
fimm. fi);  [34] ), which calculates a drug-specific sensitivity (DSS) score for each drug  [56]. DSS scores were also compared to the chemostatic effect on normal fetal astrocytes, to assess tumor specificity. Data analysis yielded five drugs that emerged as potential candidates due to their superior efficacy in tumor cells: paclitaxel, d-actinomycin, volasertib (PLK1 inhibitor), navitoclax (BCL-2/BCL-XL/BCL-w inhibitor) and I-BET-151 (a Bromodomain family inhibitor) (Supplementary Fig. 5a , online resource). These drugs, particularly paclitaxel, D-actinomycin and volasertib, showed similar effects when subsequently validated individually in a small scale tissue culture setting (Supplementary Fig. 5b , online resource), and may thus represent a starting point for future preclinical studies in this tumor type.
FLUXNET observational data We use measured or high-quality gap-filled data of soil temperature on a monthly scale from the FLUXNET2015 Tier 2 dataset to complement the model-based analysis. Detailed documentation of data and processing methods can be found in  Pastorello et al. (2020). In order to extract the potential effect of afforestation from observations, we employ a space-for-time analogy by searching for pairs of neighbouring flux towers located in forest (deciduous, evergreen or mixed trees) and open land (grasslands or croplands), respectively. This approach has been used in previous studies aiming to investigate biophysical impacts of local LUC and evaluate LSM performance  (Broucke et al. , 2015; Chen et al. , 2018). In the search for site pairs, the following criteria were defined: the two sites have to (1) be located in the EURO-CORDEX domain, (2) differ in the type of vegetation, one site being forested and the other one being either cropland or grassland, (3) have a linear distance within the horizontal resolution of the performed simulations (less than 50 km), (  4 ) have a common measurement period of at least 2 years and (5) provide measurements at common depth below the ground surface.
This was also a robust feature among the models, albeit with a considerable range in the magnitude of changes. Soil moisture decrease with afforestation resulted from large drying of deep layers, related to the fact that forests and grasslands extract soil water for transpiration processes from different soil depths. However, the homogeneous soil drying and thus the smaller soil heat capacity is not consistent with the afforestation-induced decrease of soil temperature in the majority of models, explaining only a small part of inter-model variance in AAST response in regions of central Europe. Based on paired observations from the FLUXNET dataset, we evaluated the simulated soil temperature response to afforestation. The vast majority of models agreed with the observational evidence that showed a summer ground cooling in forested areas compared to open land.
Acknowledgements. The authors gratefully acknowledge the  Swiss Confederation  for financial support through  Government Excellence Scholarship  for the academic year  2019-2020. The authors thank  Sonia I. Seneviratne  for the fruitful discussions on the progress of this study. The work of  Giannis Sofiadis  and  Eleni Katragkou  was supported by computational time granted from the  National Infrastructures for Research and Technology  S. A. ( GR-NET  S. A. ) in the National HPC facility -ARIS -under project  IDs pr005025  and  pr007033_thin. Edouard L. Davin  and  Ronny Meier  acknowledge support from the  Swiss National Science Foundation (SNSF)  through the  CLIMPULSE  project and thank the  Swiss National Supercomputing Centre (CSCS)  for providing computing resources. Rita M. Cardoso  and  Pedro M.
M. Soares  acknowledge the projects  LEADING  ( PTDC/CTA-MET/28914/2017 ) and  FCT -UID/GEO/50019/2019 -Instituto   Dom Luiz. Peter Hoffmann  is funded by the  Climate Service Center Germany (GERICS)  of the  Helmholtz-Zentrum Hereon  in the frame of the  HICSS (Helmholtz- Institut Climate Service Science ) project  LANDMATE. Lisa L. Jach  and  Kirsten Warrach-Sagi  acknowledge support by the state of  Baden-Württemberg  through bwHPC and thank the  Anton and Petra Ehrmann-Stiftung Research Training Group "Water-People-Agriculture"  for financial support. Susanna Strada  has been supported by the  TALENTS3 Fellowship Programme  ( FP  code  1718349004 ) funded by the autonomous region  Friuli Venezia Giulia  via the  European Social Fund  ( Operative Regional Programme   2014-2020 ) and administered by the  AREA Science Park (Padriciano, Italy).
Code and data availability. We used soil temperature data from the FLUXNET2015 Tier 2 dataset, which can be accessed at https://fluxnet. org/ (Pastorello et al. , 2020). Simulations were forced by the ERA-Interim reanalysis dataset (https://www. ecmwf. int/en/ forecasts/datasets/reanalysis-datasets/era-interim, Dee et al. , 2011). The source code of the Weather Research and Forecasting Model (WRF) is available by UCAR/NCAR and can be accessed at https: //www. mmm. ucar. edu/weather-research-and-forecasting-model  (Skamarock et al. , 2008). The documentation of COSMO-Model is available at the following link (https://www. dwd. de/EN/ ourservices/cosmo_documentation/cosmo_documentation. html, Sørland et al. , 2021), although a license is required for access (http://www. cosmo-model. org/content/consortium/licencing.
htm,  COSMO, 2022). The RegCM4 model is distributed from https://github. com/ictp-esp/RegCM (last access: 8 March 2021; Giorgi et al. , 2012). The source code of the REMO model is available on request from the Climate Service Center Germany (contact@remo-rcm. de) (Wilhelm et al. , 2014). All the scripts and data upon which this study is based can be accessed at https://doi. org/10. 5281/zenodo. 4588724  (Ioannis, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-15-595-2022-supplement.
Simple Summary: Despite the emergence of new therapies during the last decade, metastatic castration-resistant prostate cancer (mCRPC) remains fatal. Recent work showed that the timing of treatment initiation seems critical for patient outcomes. Thus, it is key to identify factors that can help in deciding when to start treatment. In the PROSELICA prospective international phase III trial (NCT01308580), mCRPC patients received cabazitaxel at two dose levels. We performed a retrospective analysis to determine what type of disease progression patients displayed at the cabazitaxel initiation and how this progression affected the patient's clinical outcomes. Pain progression was associated with aggressive disease and shorter survival, compared to other progression types (rise in serum PSA levels and/or alterations observed on CT scan or bone scan).
Therefore, the timing of treatment initiation seems to play a critical role. In a post hoc analysis of three randomized phase III studies in first-line mCRPC (TAX-327, VENICE, FIRSTANA), we recently reported that pain progression at chemotherapy initiation was associated with worse outcome  [20]. These findings were supported by the large international CATS registry, suggesting that clinical progression at the initiation of a life-extending therapy was associated with a shorter OS, not only in first-line mCRPC, but also in second and third-line settings, whatever the therapy (DOC, CABA, or ARTA)  [21]. Moreover, clinical progression seemed to be associated with a shorter duration of therapy with ARTA compared with taxanes. The large phase III randomized study PROSELICA (NCT01308580) evaluated the non-inferiority of two doses of CABA, at the standard dose of 25 mg/m 2 every 3 weeks (CABA25) and the lower dose of 20 mg/m 2 every 3 weeks (CABA20) in mCRPC patients previously treated with DOC  [22].
Data Collection Prostate-specific antigen (PSA) was measured at baseline and every 3 weeks during therapy. Chest, abdomen, and pelvic CT-scan or MRI were performed at baseline and every 6 weeks during therapy and repeated after 4 weeks to confirm progression. Bone scan was performed at baseline and every 12 weeks during therapy. In this post hoc analysis, all randomized patients were classified into three groups according to the type of disease progression at randomization  [22] : PSA progression only (PSA-p) was defined by rising serum levels of PSA on at least two consecutive measurements obtained at least one week apart with a value of at least 2 ng/mL, without radiological progression and without pain; radiological progression (RADIO-p) was defined by radiologic progression on CT scan or bone scan, with or without rising PSA and without pain; pain progression (PAIN-p) was defined by mean present pain intensity (PPI) ≥ 2 (McGill-Melzack questionnaire) and/or mean analgesic score (AS) ≥ 10 over the 7 days prior to randomization  [24] , with or without PSA rise, with or without radiological progression.
In any case, further investigation is required to support this proposal, such as a head-to-head comparison of patients with pain progression treated with different doses. Our study has several limitations. Although we used a phase 3 clinical trial database, our analysis was not pre-planned and the classes of disease progression used were not prespecified. However, pain at randomization was well documented and closely monitored by the mean of standardized and referenced scales over seven days before randomization, providing a robust evaluation of its intensity  [22]. PSA values were measured every three weeks, CT-scan or MRI every six weeks, and bone scan every 12 weeks, providing a robust and a reliable evaluation of PSA and radiological progression. Another limitation is that PROSELICA enrolled highly selected patients, with well-controlled comorbidities and fit enough to receive chemotherapy, which may not reflect real-life practice.
Data Availability Statement: Qualified researchers may request access to patient-level data and related documents [including, e. g. the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specifications]. Patient data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data sharing criteria, eligible studies, and process for requesting access can be found at https://www. clinicalstudydatarequest. com (accessed on 8 February 2021).
89, crystal size = 0. 05 x 0. 08 x 0. 56 m m 3, m o n o clin ic, space g roup P 21/ c , a = 16. 5221(3) A , b = 6. 9026(1) A , c = 18. 1445(3) A , V = 2011. 85(6) A 3, Z = 4, T = 100(2) K , 37915 reflection s collected , 3786 u niqu e reflections (Rint = 0. 0575), R1 = 0. 0316, w R 2 = 0. 0898 [I > 2 c(I)], R1 = 0. 0325, w R 2 = 0. 0914 [all data]. C CD C 2039456 contains the su p p lem en tary crystallographic data. These data can be obtained free of charge from T h e C am brid ge C rystallographic D ata C entre via w w w. ccdc. c a m. a c. u k /d a ta _ re q u e st/c if (accessed on 18 Febru ary 2021).
Acknowledgments: We want to thank the robotic  Bac-Screen platform  ( Faculty of Pharmacy, Mar seille ) for its contribution to screening. Authors thank  Piotr Mazur  for his irreplaceable support in the synthetic work. K. W. performed a part of microbiological studies within ERASMUS+ and ERASMUS + traineeship programs at  Saarland University (Germany)  and  Marseille University (France) , respectively.
In the following, we exclude from the land-cover translation task cases where the target is virtual and where the source and target data are not spatially overlapping. In this paper, we aim to produce a yearly updated country-scale version of the European land-cover reference database CORINE Land Cover (CLC), traditionally updated every 6 years. For that purpose, we propose a new supervised land-cover translation framework aiming to translate OSO, a France-wide yearly updated land-cover product, into CLC (Figure  1 ). In the literature, translation is an intermediate step. It is adopted either for map comparison or fusion purposes. In the first case, one aims to evaluate the quality of a map with respect to another one  [10]. In the second one, the goal is to take the best of both representations  [5, 11]  without a special focus on quality evaluation.
Water bodies Water (WAT) The CORINE Land-Cover (CLC) database is the reference European land-use/landcover database. Five versions of the product have been released so far  (1990, 2000, 2006, 2012, 2018) , covering from 27 countries in 1990  [45] , up to 39 in 2018. It is now updated every six years, accompanied by a change map and thematic by-products. CLC is based on satellite image interpretation (Landsat, SPOT, etc. ) and regional land-cover information such as aerial photographs, local knowledge, and statistics. CLC is released in vector format with a 25 ha MMU for polygons and 100 m for linear features. A raster map is also made available with a 100 × 100 m pixel resolution. The nomenclature is hierarchically organized in three levels: 5, 15, and 44 classes. The detailed nomenclature of CLC is given in Table  2.
Indeed, rare classes are often complex to discriminate even using very high spatial resolution images and multisource time series. Note that our evaluation strongly differs from the official CLC 2018 evaluation procedure  [47] , from which a 94. 2% accuracy was obtained for CLC level 3 over France (versus our 86% on level 2). This stems from multiple variations between the CLC quality assessment protocol and ours : • Two different operators double-checked the CLC official validation dataset while ours includes only one interpretation for each point. • The official validation is achieved with respect to the CLC initial segmentation (to avoid taking into account geometric errors, separately evaluated), while ours corrects wrong segmentations. Therefore, our interpretation of the same point might differ on edges.
• The official validation is performed on the vector data while ours is performed on a rasterized version, which tends to amplify CLC segmentation errors. Since we do not have access to the CLC validation dataset, all metrics are computed on our ground truth. Thus, we will not target the 94% accuracy but the 86% observed on our dataset. Evaluating the quality of the geometric preservation of a map is rarely done in landcover classification studies. Thus, we lack predefined quantitative measures to assess it. We propose to use the Edge Preservation Index (EPI), traditionally used in image reconstruction or denoising  [75, 76]. It computes the correlation between edges in a pair of images. EPI is defined as  [77] : EPI = Γ(∆p -∆p, ∆t -∆t) Γ(∆p -∆p, ∆p -∆p)Γ(∆t -∆t, ∆t -∆t) , (7) where p is the map predicted by the network, t is the ground truth target, ∆ denotes a high pass filter (we use a simple Laplacian kernel), ∆p denotes the mean of the high pass filtered image, and Γ is defined as: Γ(x, y ) = ∑ i,j ∈(nr ,nc) x (i, j ) × y (i, j ), (8) where nr and rc represent the number of pixels in rows and columns respectively of the given image.
Experiments and Discussion In this section, we investigate the translation performance of our asymmetrical U-Net on the three proposed "OSO to CLC" scenarios (Sections 5. 1-5. 3) for all of France. Our method is then compared with simpler solutions (Section 5. 1). Links for our code (https://github. com/LBaudoux/Unet_LandCoverTranslator accessed on 25 January 2021) and dataset (http://doi. org/10. 5281/zenodo. 4459484 accessed on 25 January 2021) are provided.
Consequently, some roads and small water sections are removed from the (training) data set, making the prediction of such structures more difficult. In addition, our predictions inherit systematic errors of the OSO product. For example, the OSO classes Mineral surfaces, rocks tend to be overestimated on mountain areas, resulting also in an overestimation in the CLC prediction of the non vegetated natural class. Finally, systematic errors exist on the border of the patches due to the local lack of information. These qualitative observations are quantitatively confirmed (Figure  8 ). Our first observation is that precision and recall are nearly equivalent for most classes making the method balanced. From the analysis of the confusion matrix, one might notice that the largest confusion observed is that most predicted inland wetlands are in fact inland waters.
This underlines the fact that on CLC classes with no OSO corresponding class, confusion unavoidably happens with close CLC classes. It appears impossible to improve the discrimination power of our method on this class without adding extra information. Additionally, some CLC classes such as Heterogeneous agricultural areas and Artificial nonagricultural vegetated areas are confused with a large number of other CLC classes; revealing both the need of extra information to disambiguate OSO data and the complexity of those two CLC classes (one merging multiple CLC classes, the other mixing land-use and land-cover). Using the manually labeled reference data, the obtained overall accuracy is 81%. This is slightly under the specification of CLC (>85%), under the official estimated accuracy of CLC (94.
2%), and under the accuracy of CLC on our ground truth data set 86%. A detailed per-class quantitative analysis is provided in Figure  9. The best translation performances are obtained for Urban fabric, Arable land, Permanent crops, Forest, and Non vegetated natural. The class Maritime water does not change over the various editions of CLC. We decided to provide to the network a mask for this class to ensure it focuses on finding wetlands and water bodies only. A perfect score is reported for this class but is meaningless for our analysis.
Working with the full 23 OSO labels (decomposition of Summer crops and Winter crops into a more detailed crop nomenclature) fosters a vast uprising of the ICT F1-score from the observed 0. 44 to 0. 7. Thus, we hypothesize that the observed low value stems from errors in the OSO data (some of the ICT being confused with crops). As stated previously, accuracy metrics for the classes Mine/dump/construction, Artificial non-agricultural vegetated areas and inland wetlands are computed on a small number of reference samples. In Appendix B. 1, we give a comparison between the observed F1-score agreement computed between our prediction and CLC 2018 and the previously presented F1-score computed between our prediction and our ground truth. Since CLC 2018 is not error-less, the computed values are not as reliable as the ones we compute with our ground truth.
Thus, to improve land-use class discrimination, additional data are required. While being straightforwardly integrated into our network, we discarded such solution and focused on pure context-based translation. The Edge Preservation Index in this scenario is equal to 0. 44. This might be first explained by the fact that both the true CLC and our predicted CLC tend to be more erroneous on edges. Secondly, the difficulty in predicting linear structures participates in such a low value. Thirdly, highly segmented areas correspond to specific classes where our method is prone to fail (e. g. , Heterogeneous agricultural areas).