dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collection and Preprocessing The Copernicus Sentinel 2 mission consists of two polar-orbiting satellites (Sentinel 2-A and Sentinel 2-B) that were developed to monitor variability in land surface conditions with a revisit time from 2 to 5 days according to latitude. Both satellites are equipped with an optical Multispectral Instrument (MSI) that can monitor 13 spectral bands with wavelengths ranging from 440 to 2200 nanometers at high resolution, from 10 to 60 m depending on the spectral band  (Suhet and Hoersch, 2015). Different processors have been developed for Sentinel 2 to correct images for atmospheric conditions and to provide the surface reflectance (Level 2A). One important subproduct from these processes is the pixel classification, which provides Clouds, Shadows, Land, and Water masks among other ancillary data.
Sentinel 2 -Level 2A products distributed by ESA are generated by the Sen2Cor processor  (Mueller-Wilm et al. , 2019) , while the French Land Data Center (named Theia) uses the MAJA processor  (Gascoin et al. , 2019; Hagolle et al. , 2010). Another important provider, the United States Geological Survey (USGS), generates the final pixel classification using the Function of Mask (FMask) algorithm  (Qiu et al. , 2019; Zhu et al. , 2015). To compare the performance of the proposed algorithm with the water mask provided by the FMask, MAJA and Sen2Cor processors, all 15 scenes selected within the reference dataset were downloaded from two different sources. The level-2A MAJA corrected images were downloaded from CNES's Theia Land Data Center portal (https://theia. cnes. fr/). For the FMask mask, we downloaded level-1C images from ESA's Scientific Data Hub (https://scihub.
copernicus. eu/dhus/), and for the Sen2Cor masks, we downloaded Level-2A products from the same source. Then, version 4. 0 of the FMask processor, which is available online (https://github. com/GERSL/Fmask), was used to produce the FMask categorical map. Considering the importance of the atmospheric correction to better characterize the spectral signature of the desired targets (water and nonwater) and obtain a uniform and consistent dataset  (Dinh Ngoc et al. , 2019; Jiang et al. , 2018; Wang et al. , 2018) , all of the images used to run the proposed algorithm were Level 2-A. MAJA was selected, considering the quality of its atmospheric correction and cloud mask in comparison to other processors  (Baetens et al. , 2019). To avoid undesired pixels that are not objects of this study and that could bias the results from the algorithm, one external reference mask was produced for each scene.
Five categories of pixels were masked using different specialized sources, as shown in Figure  2. The undesired categories are snow, cloud, cloud shadow, geographic shadow and ocean/coastal waters, and the respective sources for each category are listed below: • Snow: snow cover was downloaded from the Theia Snow collection  (Gascoin et al. , 2019) , which is an operational snow detector with a 20 m resolution, developed by the CESBIO laboratory; • Ocean/coastal waters: CNES ALCD Open Water Masks  (Santiago, 2019)  has a version called "inland masks" that delineates the oceans/coastal waters. This dataset was produced considering the coastal lines available at the Global Self-consistent, Hierarchical, High-resolution Geography Database -GSHHG (https://www. soest. hawaii. edu/pwessel/gshhg/) (Wessel and Smith, 1996)  at level H and using an erosion filter of 400 m toward the continent.
The shoreline was produced at a working scale of 1:100. 000 according to the World Vector Shoreline database; • Clouds and shadows: For the treatment of clouds, cloud-shadows and geographic shadows, the geophysical -MG2 mask from MAJA was selected because its multitemporal approach has been proven to be superior to other cloud detection processors, as shown in previous studies  (Baetens et al. , 2019). Although the MG2 layer is provided at 10 m and 20 m resolutions, these masks were computed at 240 m for the Sentinel-2 imagery. Figure  2. Schematic view of the sources and categories of the pixels selected to produce the reference mask for each scene.
Computational framework and infrastructure Considering the amount of data and the many experiments required to complete this study, as described in section 3, all the processing was performed on a high-performance cluster within the CNES's computational infrastructure. The main algorithm was developed in Python 3. 7. The clustering, qualification indices and machine learning algorithms were implemented using the SciKit Learning library  (Pedregosa et al. , 2011) , and the Otsu's thresholding algorithm  (Otsu, 1979)  was from the SciKit Image library  (Walt et al. , 2014). For the geospatial data manipulation, the GDAL library (GDAL/OGR contributors, 2020) was employed. The comparison task was performed using the validation module of SurfWater software developed by CNES's team, which computes the confusion matrix with more relevant parameters, including Kappa, Precision, Recall, F1 Score, among others.
Pixel sampling and generalization One disadvantage of agglomerative clustering compared to k-means is that it has a space complexity (the amount of memory needed to compute) of O(n 2 ) and a time complexity of O(n 3 )  (Firdaus and Uddin, 2015) , which make it inefficient for even medium-sized datasets. In addition, the clustering procedure is performed many times for each different K value until the method can obtain the best number of clusters. Considering that each Sentinel-2 scene includes approximately 120 million pixels (10980 by 10980 pixels) and that our objective is to provide an operational method for monitoring large areas, it would not be feasible to apply the process to the whole scene at once. The proposed method, therefore, randomly selects a subset of pixels in the scene to apply the clustering until the best K is found and the water cluster is identified.
, 2016). To overcome these limitations, another set of experiments is performed using the improved Canny-edge method introduced by Donchyt's et al. (  2016 ), which first identifies the edges and then applies the Otsu algorithm on the pixels around those edges. Both methods are implemented in Python, while taking advantage of the algorithm implementations available in the Scikit Image package  (Walt et al. , 2014). The implementation uses a mask for invalid pixels (clouds, no-data, shadows and snow). An iterative process is introduced to select the strongest edges. To provide similar conditions to the clustering technique, the final threshold is limited by preconfigured min/max values, differing, in this regard, from the original implementation.
This procedure is performed automatically by the validation module of the surf-water system developed by CNES. The results presented in Table  7  show a clear advantage for FMask and Sen2Cor over the MAJA processor, with mean kappas of 0. 764, 0. 726 and 0. 355, respectively. These results could be explained by the lower spatial resolution used by MAJA to produce the water masks and the relatively high proportion of small water body areas found in most France scenes. This comparison shows that the clustering technique outperforms the other water masks on average and, more specifically, on the scenes that have the lowest accuracy levels. Among the four scenes with the lowest kappa scores, Ariège summer, Ariège winter, Bretagne summer and Chateauroux summer only Ariège winter processed by Sen2Cor obtained a kappa value higher than 0.
Influence of water body sizes on the clustering performance To understand how the different methods are compared to each other according to the sizes of the water bodies in the scenes, we stratified the results by eight different classes, 0. 0, 0. 5, 1, 10, 50, 100, 500 and 1,000 hectares of water surface. The water surface was estimated through a polygonization procedure of the reference dataset, and each water body was accounted for separately to make an accuracy assessment. The water bodies from all of the scenes were grouped together in the analysis to obtain an overall mean for the size range across all scenes. Another important remark is that no further adjustments were made to differentiate rivers from lakes or reservoirs. In such a scenario, it is possible to have a large water surface in a thin water body, which would affect the accuracy of the algorithms by the introduction of mixed pixels, for example.
Conclusions Continuous monitoring of water surfaces is essential in many applications for water resource management, and the use of satellite images has been increasing with a higher availability of finer spatial and temporal resolution data. This study proposed and tested a new method for the automation of water pixel classification of Sentinel-2 images that combines reflectance and water indices in a multidimensional clustering approach. Additionally, a machine learning method was proposed to subsample the whole image into a smaller classification set to make the process feasible for an entire Sentinel-2 scene. The method was tested on a set of 15 scenes from France, for which the reference masks are available for download  (Santiago, 2019). The reference dataset had diverse coverage, including turbid water and some snow, which matched most European countries but did not consider extreme weather or complex canopy conditions, as in Sahelian areas, tundra plains or tropical forests.
BB2). Probing N. gruberi nuclei with fluorescence in situ hybridization (FISH) ruled out an unidentified bacterial endosymbiont as the source of the Gsp genes (Supplementary Fig. 2 ). Moreover, the eukaryotic Gsp genes usually have introns and constitute robustly supported monophyletic groups well separated from bacterial homologues (Fig. 2  and Supplementary Fig. 3 ), ruling out bacterial contamination in all cases. In an attempt to illuminate the origin of the eukaryotic Gsp proteins we carried out systematic phylogenetic analyses based on progressively expanded datasets of prokaryotic homologues and for each tree inferred the taxonomic identity of the bacterial ancestor of the eukaryotic branch (see Methods section for details on the procedure). The results, summarised in Supplementary Fig.
3 , showed that the inference is highly unstable depending on the dataset analysed, and no specific bacterial group can be identified as an obvious donor of the eukaryotic Gsp genes. This result probably stems from a combination of factors, including the long branches separating the eukaryotic and bacterial Gsp sequences, the length of Gsp proteins restricting the amount of the phylogenetic signal retained, and perhaps also rampant horizontal gene transfer (HGT) of the T2SS system genes between bacterial taxa. The eukaryotic Gsp genes are in fact so divergent that some of them could not be unambiguously classified as specific homologues of T2SS components (rather than the related machineries of the T4P superfamily) when analysed using models developed for the bacterial genomes  18  (Supplementary Fig.
8a ). These analyses revealed that seven of the families have a direct link to the T2SS suggested by discerned homology to known T2SS components. One of them represents an additional, more divergent homologue of the C-terminal part of the bacterial GspD. Hence, the protein has been marked as GspDL (GspD-like). Three other families, referred to as GspDN1 to GspDN3, proved to be homologous to the Secretin_N domain (Pfam family PF03958), present in the bacterial GspD protein as domains N1, N2 and N3 (Fig. 4a ). The N1-N3 array protrudes into the periplasm, where it oligomerizes to form three stacked rings  20. As mentioned above, the initially identified eukaryotic GspD homologues lack the N-terminal region, suggesting that the gene was split into multiple parts in eukaryotes. While GspDN1 corresponds to a full single N-domain, GspDN2 and GspDN3 relate only to its C-terminal and N-terminal halves, respectively (Fig.
Methods Sequence data and homology searches. Homologues of relevant genes/proteins were searched in sequence databases accessible via the National Center for Biotechnology Information BLAST server (https://blast. ncbi. nlm. nih. gov/Blast. cgi), including the nucleotide and protein non-redundant (nr) databases, whole-genome shotgun assemblies (WGAs), expressed sequence tags (ESTs) and transcriptome shotgun assemblies (TSAs). Additional public databases searched included the data provided by the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP  59  ) comprising TSAs from hundreds of diverse protists (https://www. imicrobe. us/#/projects/104), the OneKP project  60  (https://sites. google. com/a/ualberta. ca/onekp/) comprising TSAs from hundreds of plants and algae, and individual WGAs and TSAs deposited at various on-line repositories (Supplementary Data 4).
To further improve the sampling, draft genome assemblies were generated in this study for the heterolobosean Neovahlkampfia damariscottae and the malawimonad informally called "Malawimonas californiana". Details on the sequencing and assembly are provided in Supplementary Methods. Finally, the analyses also included sequence data from genome and/or transcriptome projects for several protists that are underway in our laboratories and will be published in full elsewhere upon completion (Supplementary Data 4). Relevant sequences were extracted from these unpublished datasets and either deposited in GenBank or included in Supplementary Data 1. Similarity searches were done using BLAST  61  (blastp or tblastn, depending on the database queried) and HMMER  62  using profile HMMs built from sequence alignments of proteins of interest.
Hits were evaluated by BLAST (blastp or blastx) searches against the nr protein dataset at NCBI to distinguish orthologues of Gsp and Gcp proteins from paralogous proteins or non-specific matches. This was facilitated by a high degree of conservation of individual eukaryotic Gsp/Gcp proteins among different species (see also Supplementary Figs. 4 and 11-13) and in most cases by the lack of other close homologues in eukaryotic genomes (the exceptions being members of broader protein families, including the ATPase GspE, the WD40 superfamily proteins Gcp1 to Gcp3 and the ubiquitin-related protein Gcp4). All identified eukaryotic Gsp and Gcp sequences were carefully manually curated to ensure maximal accuracy and completeness of the data, which included correction of existing gene models, extension of truncated sequences by manual analysis of raw sequencing reads and correction of assembly errors (for details see Supplementary Methods).
66 ; https://ihg. gsf. de/ihg/mitoprot. html), MitoFates 67 (http://mitf. cbrc. jp/MitoFates/cgi-bin/top. cgi)  67  , WoLF PSORT (https://wolfpsort. hgc. jp/) and PTS1 predictor  68  (http://mendel. imp. ac. at/pts1/). Transmembrane domains were predicted using TMHMM  69  (http://www. cbs. dtu. dk/services/TMHMM/). Homology of Gsp and Gcp protein families to other proteins was evaluated by searches against Pfam v. 31 (ref. 70 ; http://pfam. xfam. org/) and Superfamily 1. 75 database 71 (http://supfam. org/ SUPERFAMILY/index. html), and by using HHpred  44  (https://toolkit. tuebingen. mpg. de/#/tools/hhpred) and the Phyre2 server  45  (http://www. sbg. bio. ic. ac. uk/phyre2/html/ page. cgi?id=index). The relative position of the Gcp4 family among ubiquitin-like proteins was analysed by a cluster analysis using CLANS  72  (https://www.
eb. tuebingen. mpg. de/protein-evolution/software/clans/); for the analysis the Gcp4 family was combined with all 59 defined families included in the clan Ubiquitin (CL0072) as defined in the Pfam database (each family was represented by sequences from the respective seed alignments stored in the Pfam database). For further details on the procedure see the legend of Supplementary Fig. 10A. Multiple sequence alignments used for presentation of the conservation and specific sequence features of Gsp and Gcp families were built using MUSCLE  73  and shaded using BioEdit (http://www. mbio. ncsu. edu/BioEdit/ bioedit. html). In order to obtain datasets for the phylogenetic analyses of eukaryotic GspD to GspG proteins, the protein sequences were aligned using MAFFT  74  and trimmed manually. Profile hidden Markov models (HMMs) built on the basis of the respective alignments were used as queries to search the UniProt database using HMMER.
The starting dataset was prepared by a combination of two different approaches: (1) each identified sequence of Gcp1 to Gcp3 proteins was used as a query in a blastp search against the non-redundant (nr) NCBI protein database and the 500 best hits for each sequence were kept; (2) protein sequences of each the Gcp1 to Gcp3 family were aligned using MAFFT and the multiple alignment was used as a query in a HMMER3 search (https://toolkit. tuebingen. mpg. de/ #/tools/hmmer) against the UniProt database. Best hits (E-value cutoff 1e-50) from the two searches were pooled and de-duplicated, and the resulting sequence set (including Gcp1 to Gcp3 sequences) was aligned using MAFFT and trimmed manually to remove poorly conserved regions. Because WD40 proteins are very diversified, sequences that were too divergent were eliminated from the starting dataset during three subsequent rounds of sequence removal, based on a manual inspection of the alignment and phylogenetic trees computed by IQ-TREE (using the best-fit model as described above).
The final dataset was enriched by adding PEX7 and WDR24 orthologues from eukaryotes known to possess miT2SS components. The final phylogenetic tree was computed using IQ-TEE as described in the legend to Supplementary Fig. 9. IQ-TREE was used also for inferring trees of the heterolobosean 18 S rRNA gene sequences (Supplementary Fig. 1 ), ubiquitin-related proteins (Supplementary Fig. 10B ) and the Gcp16 family (Supplementary Fig. 14 ); details on the analyses are provided in legends to the respective figures. Structural homology modelling. The PDB database was searched by the SWISS-MODEL server  79  for structural homologues of GoGspD and GoGspG1. V. cholerae GspD  20  (PDB entry 5WQ9) and K. oxytoca PulG 46 pseudopilus (PDB entry 5WDA) were selected as the top matches, respectively. Models were built based on the target-template alignment using ProMod3 (Bienert et al.
Proteins extracted from these samples were then digested with trypsin and peptides were separated by nanoflow liquid chromatography and analyzed by tandem mass spectrometry (nLC-MS2) on a Thermo Orbitrap Fusion (q-OT-IT) instrument as described elsewhere  83. The quantification of mass spectrometry data in the MaxQuant software  84  provided normalised intensity values for 4,198 proteins in all samples and all three fractions. These values were further processed using the Perseus software  85. Data were filtered and only proteins with at least two valid values in one fraction were kept. Imputation of missing values, which represent low-abundance measurements, was performed with random distribution around the value of instrument sensitivity using default settings of Perseus software  85. The data were analyzed by principle component analysis (PCA).
Hybridisations were carried out overnight. Slides were incubated with tyramide reagent for 7 min. Preparations were counterstained with DAPI in VectaShield and observed under an Olympus IX81 microscope equipped with a Hamamatsu Orca-AG digital camera using the Cell^R imaging software. Heterologous gene expression, preparation of antibodies. The selected Gsp genes from G. okellyi and N. gruberi were amplified from commercially synthesised templates (Genscript; for primers used for PCR amplification of the coding sequences see Supplementary Data 6) and cloned into the pUG35 vector. The constructs were introduced into S. cerevisiae strain YPH499 by the lithium acetate/ PEG method. The positive colonies grown on SD-URA plates were incubated with MitoTracker Red CMXRos (Thermo Fisher Scientific) and observed for GFP and MitoTracker fluorescence (using the same equipment as used for FISH, see above).
For the expression in T. brucei, sequences encoding full-length GoGspD, GoGspG2, NgGspG1 as well as the first 160 amino acid residues from NgGspG1 were amplified from the commercially synthesised templates and cloned into the pT7 plasmid, encoding either three C-terminal V5 tags (full-length genes) or C-terminal mNeonGreen followed by three V5 tags (NgGspG1 targeting sequence). T. brucei cell line SMOX 927 (Poon et al. 88  ) were grown in SDM79 media  89  supplemented with 10% fetal bovine serum (Gibco). NotI-linearised plasmids (50 µg) were nucleofected into procyclic T. brucei cells using an Amaxa nucleofector (Lonza) as described before  90. Expression of the genes was induced by an overnight incubation with doxycycline (1 µg/ml). For bacterial expression, genes encoding NgGspG1 and NgGspEN2A were amplified from commercially synthesised templates and cloned into the pET42b vector (for primers used for PCR amplification of the coding sequences, see Supplementary Data 6).
4 NA oil-immersion objective. Laser wavelengths and intensities were controlled by a combination of AOTF and AOBS separately for each channel. Emitting fluorescence was captured by internal spectrally tunable HyD detectors. Imaging was controlled by the Leica LAS-X software. Images were deconvolved using the SVI Huygens Professional software (Scientific Volume Imaging) with the CMLE algorithm. Maximum intensity projections and brightness/contrast corrections were performed in the FIJI ImageJ software. Purification of native NgGspD and NgGspG. His-tagged NgGspD carrying the signal peptide of E. coli DsbA was produced in the E. coli strain BL21 (DE3) in autoinduction media (50 mM Na 2 HPO 4 , 50 mM KH 2 PO 4 , 2% Tryptone, 0. 5% Yeast extract, 85 mM NaCl, 0. 5% glycerol, 0. 05% glucose and 0.
The membrane current was registered using Ag/AgCl electrodes with salt bridges connected to an LCA-200-10G amplifier (Femto, Germany) and digitised with a KPCI-3108 16-Bit A/D card (Keithley Instruments, USA) with a 1-kHz sampling rate. Single-pore recordings were processed in the programme QuB  93. The histogram of single-pore conductance (n = 100) was constructed by kernel density estimation (with the Gaussian kernel of 100-pS width) to overcome bin edge effects. In vitro protein translation and mitochondrial protein import. The GoGspD and NgGspD genes were amplified from commercially synthesised templates (for primers used for PCR amplification of the coding sequences, see Supplementary Data 6) and cloned into pDHFR vector provided in the PURExpress In Vitro Protein Synthesis Kit (NEB).
Acknowledgements We would like to thank  Michelle Leger  and  Alastair Simpson  for granting us access, prior to publication, to their transcriptomic data from A. godoyi and G. okellyi, respectively, data that were instrumental in annotating the Gsp and Gcp genes in our genome assemblies. This work was supported by  Czech Science Foundation  grants  13-29423S  to P. D. and  18-18699S  to M. E. , and the  KONTAKT II  grant  LH15253  provided by  Ministry of Education, Youth and Sports of CR (MEYS)  to P. D. ; This work was also supported by  MEYS  within the  National Sustainability Program II (Project BIOCEV-FAR ,  LQ1604 ) the project  BIOCEV  ( CZ. 1. 05/1. 1. 00/02. 0109 ),and the project " Centre for research of pathogenicity and virulence of parasites " (No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000759 )funded by  European Regional Development Fund (ERDF)  and  MEYS  and by Moore-Simons Project on the Origin of the Eukaryotic Cell to P.
D. https://doi. org/10. 37807/GBMF9738; The work in the OF laboratory was funded by the  ANR-14-CE09-0004  grant. J. P. was supported by a grant from the  Gordon and Betty Moore Foundation  to ADT. This work was supported by  The Ministry of Education, Youth and Sports  from the  Large Infrastructures for Research, Experimental Development and Innovations  project " IT4Innovations National Supercomputing Center -LM2015070 ".