dataesr/scientific-paragraphs-categorization huggingface dataset:
CYGNSS Data Normalisation Several studies have highlighted the influence of incidence angle on reflectivity, and in the present study, the role of this effect needs to be assessed for different types of surface topography. In Figure  2 , the computed reflectivity derived from Equation (  1 ) is plotted as a function of the incidence angle for two different areas with the whole temporal dataset. These test areas are described in Table  2. present study, the role of this effect needs to be assessed for different types of surface topography. In Figure  2 , the computed reflectivity derived from Equation (  1 ) is plotted as a function of the incidence angle for two different areas with the whole temporal dataset. These test areas are described in Table  2. It can be seen that the incidence angle has a very limited effect for incidence angle values between 2° and 30°.
As shown in Figure  2 , a simple linear normalization is thus proposed, referring to an incidence angle of 20°, in order to account for the small influence of the incidence angle on the reflectivity. We use the following expression: Γ(20°) = Γ(θ) -A. (θ -20°) (3) where the parameter A is the slope of the retrieved least squares linear relationship between reflectivity and incidence angle for a given type of surface. In our context, with a limited time series of CYGNSS data, the azimuth angle effect is not considered in the proposed data analysis. This effect is negligible for land surfaces, particularly after data temporal averaging.
Reflectivity Mapping over Sahara Following the normalization of the CYGNSS measurements and validation of the stability of the Sahara's reflectivity during the studied period, the data were arranged in a grid format, each pixel of which corresponds to the mean value over the full period of observation; i. e. , nearly 2. 5 years. The challenge of this process is to retain the finest possible grid resolution, with the constraint of using a sufficiently large number of observations to ensure the accuracy of the resulting average value. For this purpose, three different resolutions were tested. Figure  4  shows the reflectivity histograms obtained with resolutions of 0. 1 • , 0. 05 • and 0. 03 • (close to 3. 5 km), and simple Gaussian distributions fitting the experimental data. It can be seen that this approximation is better adapted to the Sudan-Nile site (Figure  4b ) than to the Kufrah site (Figure  4a ).
This could be explained by the presence of strong heterogeneities at the former site, which includes both flat and mountainous areas. Once the 0. 03° resolution was selected, a minimum number of measurements (four CYGNSS data points) was used to achieve a reliable time average for each pixel. This threshold was determined empirically, using qualitative observations of retrieved maps, and our analysis of the sensitivity of the estimated reflectivity to the number of measurements. Grid points that were not covered by four measurements were not included in the final grid. These points correspond mainly to high altitude zones (>650 m), for which the data were not analyzed (see above regarding data filtering). Figure  5  shows the resulting reflectivity map at a resolution of 0. 03° × 0. 03°, which covers the latitudes between 15°N and 37°30'N and the longitudes between 17°W and 39°30'E.
This empirical result shows that we can distinguish details, which are larger than the Fresnel zone, but also much smaller than the resolution of the incoherent signal. Once the 0. 03 • resolution was selected, a minimum number of measurements (four CYGNSS data points) was used to achieve a reliable time average for each pixel. This threshold was determined empirically, using qualitative observations of retrieved maps, and our analysis of the sensitivity of the estimated reflectivity to the number of measurements. Grid points that were not covered by four measurements were not included in the final grid. These points correspond mainly to high altitude zones (>650 m), for which the data were not analyzed (see above regarding data filtering). Figure  5  shows the resulting reflectivity map at a resolution of 0.
Results and Discussions In this section, we focus on the relationship between the CYGNSS reflectivity and the large-scale roughness observed over different types of the Sahara environment. We discuss the influence of geometric roughness and the aerodynamic roughness length Z 0 on the signals received by CYGNSS. As SAR data are often used to map roughness in deserts and other regions of the globe, it is worthwhile to carefully compare the CYGNSS reflectivity results with SAR roughness measurements derived from ALOS-2 backscattering coefficients.
Results and Discussions In this section, we focus on the relationship between the CYGNSS reflectivity and the large-scale roughness observed over different types of the Sahara environment. We discuss the influence of geometric roughness and the aerodynamic roughness length Z0 on the signals received by CYGNSS. As SAR data are often used to map roughness in deserts and other regions of the globe, it is worthwhile to carefully compare the CYGNSS reflectivity results with SAR roughness measurements derived from ALOS-2 backscattering coefficients.
ardabiliae (Za17) and Z. passerinii (Zpa63) genomes included no fully assembled chromosomes, but twelve and ten contigs respectively with telomeres at one of the ends (Table  1 ; Figure  S1 ). The transcriptome-based gene predictions for these new assemblies include between 10,528 and 12,386 protein-coding genes (Table  S2 ). This range is consistent with the annotation of the reference genome IPO323 reporting 11,839 protein-coding genes  [22]. We used Benchmarking Universal Single-Copy Orthologs (BUSCO) from the lineage dataset Pezizomycotina to evaluate the completeness of the assemblies and gene predictions  [37]. The proportion of complete BUSCO genes identified in our assemblies were comparable to the one obtained for the reference genome of Z. tritici Fig. 1  Whole-genome phylogeny of Zymoseptoria spp.
and basic statistics for the assemblies and gene predictions. a Tree based on the distance matrix generated by the software andi distances from whole genome sequences were estimated in an alignment-free manner  [35]. The same topology was observed in a tree produced from k-mers using the web-based tool CVtree3  [36]. b The bar plots represent the number of genes coding for secreted proteins (pink) and non-secreted proteins (grey) for each genome (97. 8%, Table  1 ). The assessment of gene content completeness (see Methods) indicates that, despite more fragmented assemblies of Z. ardabiliae and Z. passerinii, the genomes are complete in terms of gene content and that the unassembled fragments are more likely to comprise repeats and not protein-coding genes. Based on the whole-genome sequences and the predicted genes we reconstructed the phylogeny of the Zymoseptoria genus using the publicly available genome of Cercospora beticola as an outgroup  [38, 39].
Discussion In this study we present a new resource of high-quality whole genome assemblies and gene annotations for the fungal grass pathogens Z. ardabiliae, Z. brevis, Z. passerinii, Z. pseudotritici, and two isolates of the wheat pathgoen Z. tritici. This new dataset provides a valuable resource for detailed analyses of genome architecture and evolutionary trajectories in this group of plant pathogens. Here, we conduct some detailed comparative analyses of genome architecture and show a considerable extent of variation in sequence composition during the recent evolution of the Zymoseptoria lineages. We show that genome compartmentalization and accessory chromosomes represent shared ancestral traits among these pathogen species. We identify extensive presence-absence variation of protein coding genes in genomes of the five Zymoseptoria species consistent with the variable gene repertoire already reported for one of the species, Z.
Genome assembly, and repeat and gene predictions For each isolate, we assembled the genome de novo using SMRT Analysis software v. 5 (Pacific Bioscience) with two sets of parameters: default parameters and "fungal" parameters. We chose the best assemblies generated by comparison of all assembly statistics produced by the software Quast such as the number of finished contigs, the size of the assembly and the N50  [47]. Summary statistics for each assembly can be found in Table  1. In order to exclude poor quality contigs from the raw assemblies, we filtered out the contigs with less than 1. 5X and more than 2X median read coverage as these might be unreliable from lack of data or because they contain only repeated DNA  [34]. This filter removed a high number of contigs, i. e. , between 58 and 17% of contigs, but a only a small quantity of base pairs as compared to the genome size (Table  S1 ; S5).
In order to identify the number of fully assembled chromosomes or chromosome arms, we investigated the presence of telomeric repeats ("CCCTAA") in the assembled contigs using bowtie2 and recorded the presence of more than six repeats at the contig extremities  [48] [49] [50]. We reported for each contig the number of such blocks of telomeric repeats and considered a contig flanked by these repeats on both sides to be a fully assembled chromosome. We next used the REPET package to annotate the repeat regions of Z. ardabiliae Za17, Z. brevis Zb87, Z. pseudotritici Zp13, Z. passerinii Zpa63, and the Z. tritici isolates IPO323, Zt05 and Zt10 (https://urgi. ver sailles. inra. fr/Tools/REPET [51, 52] ). For each genome, we annotated the repetitive regions as follows: we first identified repetitive elements in each genome using TEdenovo following the developer's recommendations and default parameters.
For our third approach, the RNA-seq reads were separately assembled into gene transcripts using Trinity  [58]. These were aligned using PASA and EVidence Modeler to produce consensus gene models from the two independent predictions and the de novo assembled transcripts  [59]. Gene counts, length and other summary statistics presented in Table  S1  and S3  were obtained using GenomeTools  [60]  and customs scripts (https://gitlab. gwdg. de/alice. feurtey/genome_archi tecture_zymoseptoria). The predicted gene sequences were the basis for an evaluation of the completeness of the assembly and gene prediction by the program BUSCO v. 3  [37]. We used this method with the lineage dataset Pezizomycotina. The predicted genes were also used to create a phylogeny with the online implementation of CVtree3, using kmer sizes of 6 and 7 as recommend for fungi  [36].
We generated a second tree with the whole assemblies, estimating a distance matrix using the andi software  [35]. We predicted orthologs between the newly assembled genomes, the reference Z. tritici genome and the reference Cercospora beticola genome, a related Dothideomycete, which we used as outgroup to identify genes with orthologs restricted to the Zymoseptoria genus  [21]. For this, we used the software PoFF  [39, 61]  which takes into account synteny information in the analyses of similarity inferred by the program Proteinortho  [39]. These orthogroups were used to visualize synteny between genomes using Circos  [62]. The whole-genome assemblies were used to create a matrix distance with the software andi, from which we generated a tree  [35]. A second tree was generated from the gene prediction with the online implementation of CVtree3  [36].
Functional annotations We used several tools to predict the putative functions for the gene models. First, we used the eggnog-mapper which provide COG, GO and KEGG annotations  [63]. The online resource dbCAN2 was run to identify carbohydrate-active enzymes (CAZymes)  [64]. Finally, for each genome, we used Antismash v3 (fungal version) to detect biosynthetic gene clusters (Figure S1  [65] ). Additionally, we designed a pipeline to predict protein cell localization and to identify effector candidates. The pipeline for effector prediction is outlined in Figure  S1  and includes the software DeepLoc  [66] , SignalP  [67] , TargetP  [68] , phobius  [69]  and TMHMM  [70, 71] , which predict the cellular location, the peptide signals and whether proteins are transmembrane. Effector candidates were identified with EffectorP v2 which uses both a new machine learning approach and more complete databases to improve effector prediction compared to the previous version  [19].
The pipeline also includes software which are specifically targeted to annotate plant pathogenic functions, namely the program ApoplastP  [72]  and LOCALIZER  [73]. We wrote wrappers scripts, which run the software and create consensus between the different prediction tools providing one command line from the user. These scripts are available at https:// gitlab. gwdg. de/alice. feurtey/genome_architecture_zymo septoria. Briefly, we gathered outputs of several software to predict the cellular location, transmembrane domain and secretion and created a consensus based on the different output to prevent the pitfalls of any one of these methods. From this consensus, we extracted the gene products predicted to be secreted and without a transmembrane domain. The comparisons of genes functions repartition were done by combining predictions of COG categories, secondary metabolite genes with pathogenicity-related gene functional categories such as CAZymes and effector predictions.
Gene expression analyses To update expression profiles on the new genome assemblies and new gene predictions of the three Z. tritici isolates IPO323, Zt05 and Zt10, we used previously generated RNA-seq data from in planta and in vitro growth  [30, 32]. The in planta RNA-seq data was obtained from infected leaves at four different stages corresponding to early and late biotrophic and necrotrophic stages of the three Z. tritici isolates  [30]. Strandspecific RNA-libraries were sequenced using Illumina HISeq2500, with 100pb single-end reads for a total read number ranging from 89. 5 to 147. 5 million reads per sample. This data was previously analyzed  [30] , using gene predictions generated from an Illumina-based  [22]. The reads were here mapped on the new assemblies of Zt05 and Zt10 and the reference genome of IPO323 after trimming.
We used the DESeq2 R package to determine differential gene expression during in planta infection, considering only two infection stages; biotrophic and necrotrophic  [74]. Gene expression was assessed as Transcript per Million (TPM). Briefly, TPM is calculated by normalizing read counts with coding region length resulting in the number of reads per kilobase (RPK). RPK total counts per sample are then divided by 1 million to generate a "per million" scaling factor. We calculated the coding region length of each gene with GenomicFeatures R package using the function called "exonsBy"  [75]. For gene expression analyses, we further filtered our gene predictions to remove any predicted transposases and other TE-related annotations based on the Eggnog mapper annotations. ChIP-sequencing and data analysis Z.
ardabiliae (Za17) and Z. pseudotritici (Zp13) cells were grown in liquid YMS medium for 2 days at 18 °C until an OD 600 of ~1 was reached. Chromatin immunoprecipitation and library preparation were performed as previously described  [76]. We sequenced two biological and two technical replicates per isolate and used antibodies against the euchromatin histone mark H3K4me2 (#07-030, Merck Millipore). Sequencing was performed at the OSU Center for Genome Research and Biocomputing (Oregon State University, Corvallis, USA) on an Illumina HiSeq2000 to obtain 50-nt reads. The data was quality-filtered using the FastX toolkit (http://hannonlab. cshl. edu/fastx_toolkit/), mapping was performed using bowtie2  [77]  and peaks were called using HOMER  [78]. Peaks were called individually for each replicate, but only peaks that were detected in all replicates were considered and merged for further analysis.
Funding CL is funded by the  Institut national de la recherche agronomique (INRA)  in the framework of a " Contrat Jeune Scientifique " and by the  Labex ARBRE (Lab of Excellence ARBRE). Genome research in the group of EHS is funded by the  DFG   Priority Program   SPP1819  and the  Canadian Institute for Advanced Research (CIFAR). AF is funded by by the  DFG Priority Program   SPP1819.
Availability of data and materials The datasets generated and analyzed during the current study are available in the NCBI Short Read Archive (https://www. ncbi. nlm. nih. gov/bioproject/ ?term=) under the BioProject accession numbers: PRJNA638605, PRJNA639021 (Zpa63), PRJNA638553 (Zb87), PRJNA638515 (Zp13), PRJNA638382 (Za17), PRJNA414407 (Zt05 and Zt10). All the data supporting the findings of this study are openly available at https://doi. org/10. 5281/ zenodo. 3820378. The assembled genomes can be found at https://doi. org/ 10. 5281/zenodo. 3820378. The gene annotations are deposited at https://doi. org/10. 5281/zenodo. 3820378. The functional annotation pipeline, additional scripts and command lines used to create the results presented in this manuscript can be found at https://gitlab.
gwdg. de/alice. feurtey/genome_ architecture_zymoseptoria. In planta RNA-seq datasets for Z. tritici IPO323, Zt05, Zt10 from  [30]  are available at the accession number NCBI Gene Expression Omnibus GSE106136 and in vitro RNA-seq data for Za17, Zp13 and Zb87 from  [22]  are available at the accession number NCBI BioProjects PRJNA277173, PRJNA277174, and PRJNA277175. The genome sequence of the reference isolate IPO323 available at: http://genome. jgi. doe. gov/Mycgr3/ Mycgr3. home. html. The ChIP-seq dataset are available under the BioProject accession PRJNA640982 for Za17 and Zp13. The ChIP-seq datasets for the reference genome IPO323 from  [6]  are available under the accession number PRJNA286790.
4 98. 7 98. 5 97. 0 98. 2 97. 5 Number of genes 12,386 11,991 11,661 11,480 11,463 10,528 Repeat content (%) 19. 9 16. 5 20. 8 29. 2 18. 2 31. 4 Percentage of non-core 12. 49 8. 69 4. 27 4. 6 3. 45 6. 85 sequences compared to the IPO323 reference NCBI Biosample SAMN04494882 SAMN02981321 SAMN02981322 SAMN03294124 SAMN02981326 SAMN02981330.
Second, to detect monotonic temporal trend, Mann-Kendall trend tests were used on the densities of probability over the period for each pair of water temperature and discharge conditions. The smoothness of the kernel density curves depended on the value of the bandwidth parameter (H), which was selected according to the plug-in method  (Chacón and Duong, 2010; Duong and Hazelton, 2003 ; see an in-depth description of the bandwidth's choice in Appendix B). Statistical significance was assessed by comparing results with those from artificial datasets generated under the null hypothesis. The null hypothesis implied that the frequency of a given water temperature and discharge association did not change over time while the alternative hypothesis implied a more or less frequent association over time.
Two methods were proposed to simulate artificial datasets under null hypothesis, permutations and sampling in kernel, but since both methods provide similar results, we only focused on the second method (see Appendix A, Fig. A1 ). To visualise how the associations between the two variables have changed over the study period, two-dimensional heatmaps were drawn with water temperature on the x-axis and log-discharge on the y-axis. On these heatmaps, blue areas indicate water temperature and discharge associations that have become more frequent (positive Mann-Kendall's tau coefficient), while red ones indicate water temperature and discharge associations that have become less frequent (negative Mann-Kendall's tau coefficient). Dark colored areas indicate statistically significant trends, while light colored ones indicate non-significant trends.
The plot region was restricted to the convex hull that encompassed all points in the dataset to avoid extrapolation. Then, the area of regions showing a significant negative or positive trend was divided by the total area of the convex hull to obtain an indicator (%) of water temperature and discharge associations that became more or less frequent over the study period. This approach was applied on the entire dataset (annual scale) and on predefined ecological seasons (seasonal scale). Four seasons were defined according to the life-cycle of most diadromous species: winter from January to March, spring from April to June, summer from July to September and fall from October to December. The entire methodology was implemented in the R software "ChocR" package (R Development Core Team, 2019).
, 2008). • For twaite shad (Alosa fallax), spawning migration takes place from February to May when water temperature exceeds 10 °C  (Aprahamian et al. , 2003; Keith et al. , 2011). Reproduction is observed between May and June, when water temperature is between 18°and 22 °C  (Keith et al. , 2011). Then, after 1-3 months' growth in freshwater, juveniles migrate to the estuary in fall. Downstream migration of allis and twaite shad juveniles is closely associated with fall floods  (ICES, 2015). Twaite shad in the Mediterranean area (Rhône River in the present dataset) belong to a specific sub-species, Alosa fallax rhodanensis, distinct from the Alosa fallax fallax sub-species found elsewhere  (Keith et al. , 2011 ). • The one catadromous species considered here, the European eel (Anguilla anguilla), migrates downstream to spawn at sea mainly in fall and early winter, although this period sometimes extends into spring, especially in fragmented rivers  (Acou et al.
, 2020). The Fig. 3. Two-dimensional space of log-transformed discharge (Y-axis; log m 3 s -1 ) and water temperature (X-axis; °C) for each river at annual and seasonal scales. Water discharge and temperature associations that have become more or less frequent over the study period are shown in blue or red, respectively. Light or dark colors correspond to non-significant and significant trends, respectively (e. g. , dark red corresponds to significant decreasing trends, light blue to non-significant increasing trends). The gray line delineates the edge of the convex hull (i. e. , the smallest space encompassing all the points of the annual or seasonal datasets). For Atlantic salmon , European eel , sea lamprey , European river lamprey , European sturgeon , allis shad and twaite shad , the season(s) of upstream migration (in black), spawning (in gray) and downstream migration (in white) are specified for the river(s) in which the species is present.
Acknowledgments The present study was funded by the  HYNES program  (IRSTEA -EDF R&D). We would like to thank  Électricité de France (EDF)  for their financial support and for providing part of the data. We also would like to thank the MIGADO association for providing part of the data,  Stéphanie Mahévas ,  Etienne Rivot ,  Benjamin Planque ,  Nicolas Bez  and  Jean-Marc Fromentin  for their help in the development of the analytical methods,  Pascal Laffaille ,  Hervé Capra  and  Eric Feunteun  for their fruitful remarks about the overall approach and  Iain McGill  for professional proofreading of the English text. We are thankful to the referee and editor for their constructive comments.
Moreover, we show how the reinterpretation results, with uncertainties included, could be correctly extrapolated to different luminosities to get insight on the sensitivity of the future LHC data on given signals. As an illustration of these new features within concrete cases, we consider several classes of widely used simplified models. We first extract bounds on various model parameters from recent LHC results. Next, we study how those constraints are expected to evolve with the upcoming high-luminosity run of the LHC through a naive rescaling of the signal and background predictions. In practice, we make use of the recasting capabilities of MadAnalysis 5 and pay a special attention to the theoretical uncertainties. We begin with a simplified model inspired by the Minimal Supersymmetric Standard Model (MSSM) in which the SM is complemented by a gluino and a neutralino, all other superpartners being assumed heavy and decoupled  [9, 10].