dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are most grateful to the  Genomics and Bioinformatics Core Facility of Nantes (GenoBird, Biogenouest) , the MicroPICell platform and the animal facility staff for their technical support (UTE, SFR). INSERM,  Ligue Contre le Cancer Grand Ouest , "ERRATA" from the  Région Pays de la Loire  and  HTE Moglimaging program  (F. V. ). A. A. A. was supported by a fellowship from  UCLM  [ 2015/4062 ]. This work was realized in the context of the  LabEX IGO program  supported by the  National Research Agency  via the investment of the future program  ANR-11-LABX-0016-0. M. R. was supported by a fellowship of the  French Research Minister.
ASVMvdH drafted the original manuscript, all authors contributed in reviewing, editing, and approving the final manuscript. Funding This study was funded by  KWF Kankerbestrijding  ( UL2011-5336  and  12376 ). Competing interests ASVMvdH and NH report a research grant from the  Dutch Cancer Society , during the conduct of the PORTEC-4a study. HWN reports nonfinancial support from  Merck , grants from the  Dutch Cancer Society , grants from  AIMM , outside the submitted work. RAN reports grants from the  Dutch Cancer Society , grants from the  Dutch Research Council , grants from  Elekta , grants from  Varian , grants from  Accuray , outside the submitted work. CC reports non-financial support from  Roche , non-financial support from  TherAguix , personal fees from  Elekta , personal fees from  MSD , personal fees from  GSK , outside the submitted work.
Empirical hydrological uncertainty processor (EHUP) We used the empirical hydrological uncertainty processor (EHUP) presented in  Bourgin et al. (2014). It is a databased and non-parametric approach in order to estimate the conditional predictive uncertainty distribution. This postprocessor has been compared to other post-processors in earlier studies and proved to provide relevant results  (Bourgin, 2014). It is now used by operational FFS in France under the operational tool called OTAMIN  (Viatgé et al. , 2019). The main difference with many other post-processors (such as the MCP, the meta-Gaussian processor, the BJP or the BFS) is that no assumption is made about the shape of the uncertainty distribution, which brings more flexibility to represent the various forecast error characteristics encountered in large-sample modelling.
We will discuss the impact of this choice in Sect. 4. 2. The basic idea of the EHUP is to estimate empirical quantiles of errors stratified by different flow groups to account for the variation of the forecast error characteristics with forecast magnitude. Since forecast error characteristics also vary with the lead time when data assimilation is used, the EHUP is trained separately for each lead time. For each lead time separately, the following steps are used: 1. Training: -The flow groups are obtained by first ordering the forecast-observation pairs according to the forecasted values and then stratifying the pairs into a chosen number of groups (in this study, we used 20 groups), so that each group contains the same number of pairs. -Within each flow group, errors are calculated as the difference between the two values of each forecastobservation pair, and several empirical quantiles (we used 99 percentiles) are calculated in order to characterise the distribution of the error values.
Selection of the data subsets The selected events were then gathered into three events sets -G1, G2 and G3 -based on the magnitude of their peaks and the number of useful time steps for each test phase (training of the EHUP post-processor, calibration of the variable transformations and evaluation of the predictive distributions): G1 contains the lowest events, while the highest events are in G3. The selection of the data subsets was tailored to study the behaviour of the post-processing approach in an extrapolation context. The control data subset had to encompass only time steps with simulated discharge values higher than those met during the training and calibration steps. Similarly, the calibration data subset had to encompass time steps with simulated discharge values higher than those of the training subset.
Earth Syst. Sci. , 24, 2017-2041, 2020 www. hydrol-earth-syst-sci. net/24/2017/2020/ ). First, the events are selected (grey highlighting). Then, the four data subsets are populated according to the thresholds (horizontal dashed lines). See Sect. 2. 1. 3 for more details. where N is the number of time steps, F (Q) is the predictive cumulative distribution, H the Heaviside function and Q obs is the observed value. We used a skill score (CRPSS) to compare the mean CRPS to a reference, here the mean CRPS obtained from the unconditional climatology, i. e. from the distribution of the observed discharges over the same data subset. Values range from -∞ to 1 (perfect forecasts): a positive value indicates that the modelling is better than the climatology. For operational purposes, the sharpness of the probabilistic forecasts was checked by measuring the mean width of the 80 % predictive intervals.
Investigating the performance loss in an extrapolation context For operational forecasters, it is important to be able to predict when they can trust the forecasts issued by their models and when their quality becomes questionable. Therefore we investigated whether the reliability and reliability loss observed in an extrapolation context were correlated with some properties of the forecasts. First, Fig. 14  shows the relationship between the α-index values obtained with D2 sup and those obtained with D3 for three representative transformations. The results indicate that it is not possible to anticipate the α-index values when extrapolating high flows in D3 based on the α-index values obtained when extrapolating high flows in D2 sup. In addition, two indices were chosen to describe the degree of extrapolation: the ratio of the median of the forecasted discharges in D3 over the median of the forecasted discharge in D2 sup , and the ratio of the median of the forecasted discharges in D3 over the discharge for a return period of 20 years (for catchments where the assessment of the vicennial discharge was available in the national database: http://www.
Acknowledgements. The authors thank the editor  Dimitri Solomatine ,  Kolbjorn Engeland  and one anonymous reviewer. Both reviewers provided insightful comments which helped to greatly improve this text. Thanks are also due to  Météo-France  for providing the meteorological data and Banque HYDRO database for the hydrological data. The contribution of the authors from  Université Gustave Eiffel  and  INRAE  was financially supported by  SCHAPI (Ministry of the Environment). Financial support. This research has been supported by  SCHAPI (Ministry of the Environment)  (grant nos. 21367400  ( 2018 ), 2201132931 (2018) and 2102615443 (2019)). Review statement. This paper was edited by  Dimitri Solomatine  and reviewed by  Kolbjorn Engeland  and one anonymous referee.
Data availability. Data for this work were provided by Météo France (climatic data) and the hydrometric services of the French government (flow data). Readers can freely access streamflow observations used in this study at the Banque HYDRO website (http: //www. hydro. eaufrance. fr/, last access: April 2020). Climate data can be retrieved on the Publithèque website of Météo France (https: //publitheque. meteo. fr/, last access: April 2020). These data are not publicly available but can be provided for research and nonprofit purposes -upon request to Météo France. Metadata on the catchments used in this study are detailed by  Brigode et al. (2020)  (https://doi. org/10. 15454/UV01P1). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-2017-2020-supplement.
Brazil ranks 4 th in the world for occupational accidents. There are more than 700 thousand occurrences per year, which cost the country about R$10 billion. The objective of this study is to identify technologies, related to the prevention of occupational accidents and diseases, that are not protected in Brazil. We used Patent2net, a computational data mining tool, to evaluate worldwide patents deposited in the Espacenet database. Employing a technometric research approach, a Patent2net case study was carried out by analyzing over 1,600 occupational safety and/or occupational disease patents deposited in Espacenet. Notably, a Chinese utility model patent describing a helmet with a breathing mask, that provides the worker protection against the inhalation of chemical agents and head overheating.
Based on this information, an economical path forward, for R&D, is identifying and utilizing these free-use patents  (OLIVEIRA, 2003). Barros et al. (2015)  alerted the importance for Brazil to internalize technologies developed in other countries through the documentary recovery of patent information. The authors went on to demonstrate that the study of patents can assist in monitoring the technological advancement of competing products, geo-identifying company activity, benchmarking for licensing and technology transfer, among others  (BARROS et al. , 2015). The Espacenet database is a free online patent search service. It was developed and is maintained by the European Patent Office (EPO), offers more than 100 million patent documents, from more than 80 countries, including Brazil, and enables the user to research the text of documents in their entirety  (FERRAZ et al.
, 2016). Using this database, the present study sought to answer the following research questions: Can Patent2net, a computational data mining tool used to retrieve information and patent records, identify a free-use technology that minimizes occupational respiratory diseases? If so, could this technology be reproduced in Brazil? The justification for the investment in this topic is the possibility of offering an explanatory and theoretical study about the proposed search method, and to shed light on the advantages and improvements a tool like Patent2net offers. The present study obtained information related to the innovation of a product that can provide workplace protection and reduce the development of respiratory diseases, often diagnosed in miners.
Another important aspect concerns international law, which allows a patent protected in a certain country to be freely used in any other territory where it is not protected  (JANKE, 2003). This justifies the existence of extensive patent databases that can be consulted, and from which ideas for the development of new technologies or products, based on previously patented innovations, are obtained  (BARROS et al. , 2015). Barros et al. (2015)  showed that "(. ) of 1,781,768 patent applications from a search in the Patentscope database (2017), only 2% of the deposits were from Brazil". This low percentage reflects Brazil's need for innovation and patent output, which will almost certainly be beneficial, as seen in many other countries. Mining patent databases, data mining, allows the user to collect information related to patents on a specific subject, using complex statistical and mathematical techniques  (LAROSE, 2005).
Patent2net Patent2net is a free tool used for data mining in Espacenet. It extracts information directly from the Espacenet database, assembles graphs and dynamic tables and facilitates the selection of patents of interest  (FERRAZ et al. , 2016). Additionally, it is an open source patent tracker of the Espacenet database, developed and fostered through academic work carried out by an international multidisciplinary team, which groups together about twenty researchers and scholars, teachers and students from nations such as Brazil, Algeria, Senegal, France, Switzerland and Portugal (PATENT2NET, 2016). According to  Ferraz et al. (2016) , Patent2net performs the data mining process efficiently and effectively, taking into account the speed, volume and variety of the information processed during the research, while at the same time complying with the legal precepts related to the search for information in patented documents.
METHODOLOGY The present study focused on respiratory protection in the workplace, and analyzed patent registrations using a data mining process. Patent documents constitute important references of the chosen data collection method. In addition, it is important to understand the importance of the technological development, as well as to support the proposal of public policies and/or industrial strategies  (SANTOS et al. , 204). Simply put, the patent describes in detail the method for reproducing the invention (ABBAS; ZHANG;  KHAN, 2014). This research is classified as quantitative, descriptive and exploratory, which in this particular case is defined as technometric  (CIRIBELLI, 2003). Quantitative data on inventions are presented and are based on the volume of patents related to the subject of interest deposited on the Espacenet database  (KÖCHE, 2011).
We employed Patent2net to sequentially run several specific computational modules, mining the Espacenet database for texts and registries on patents related to the "prevention of accidents and occupational diseases ", and then analyzed the selected patents. For data collection, the following search strategy (string) was provided to the tool: (ta = "job securit*") OR (ta = "work securit*") OR (ta = "job safet*") OR (ta = "work safet*") OR (ta = "occupat* diseas*"). Briefly, the software searched Espacenet, in the title or in the abstract (ta =, being "t" for title and "a" for abstract), the terms presented in the search string, defined by time in consultation with a professional in the safety area at work. Following collection, data was analyzed and presented in the form of graphs and reports, generated by Patent2net.
This allowed for the quantitative evaluation of the patented documents on the proposed topic, and made it possible to identify available technologies that can be replicated in countries where the patents do not have extended protection (i. e. Brazil). Part of the results presented can be classified as technometric, since the data obtained can be used to analyze and construct indicators. The analysis was performed using libraries or open source computing packages, such as Gephi (networks), data table (displaying or searching), Iramuteq (textometry) and Freeplane (Mind Maps), which were all coupled to the central software. The data mining described in this research lasted for approximately 24 uninterrupted hours, on a personal computer with 16Gb of RAM, an I7 processor, internet connection, and an approximate download speed of 96GB.
The seventh document, label CN204395257 (Figure  3 , red arrow), was found to be the most appropriate, and described a "Construction helmet with air supply and dust filter". This patent was filed on 06/17/2015 and is classified as a utility model. Based on its label, the full document was identified in the Espacenet database (Figure  4 ). This document contemplates technologies with IPCR7 of the group A62B18, and concerns the capacity of dual utility protection of both the respiratory tract and the head of the worker, both indispensable especially in the civil construction and mining. As shown in Figure  5 , the priority period for this patent expired in October 2016, and is therefore free to consult in full, by simply accessing the link "Original document" (indicated by the red arrow). In the English abstract available at the bottom of the screen (Figure  5 ), it was possible to identify that the described technology functioned as a shield against head contusions and overheating, as well as respiratory tract protection, since the helmet is coupled to an exhaust system that removes dust and other particles suspended in the air.
FINAL CONSIDERATIONS, LIMITATIONS AND PROPO-SALS FOR FUTURE RESEARCH In Brazil, accidents and occupational diseases are rampant, and can force workers to be removed from their jobs. The present study identified a free-use patent that could be reproduced in Brazil, and, more specifically, a patent that could reduce the inhalation of particles in the air, thereby reducing the risk of developing pneumoconiosis. In order to answer the proposed research questions proposed, we used the Patent2net software to extract, organize and present information related to patents deposited in the Espacenet database, on occupational safety and diseases. From the case study described here, a patent for a helmet and anti-particle mask were identified. It was verified that, in view of the need to stimulate innovation-oriented research, especially with increased investments (not always available to private companies), and with the stimulus to the research by means of the granting of fomentation of the public organs, in times of a claudicating economy, Patent2net proved to be an effective tool in the provision of inventions that are free for reproduction in Brazil, and which could be used to protect the worker against pneumoconioses, providing transparency to the information in the patent registers, which until then were only available in a static.
The following criteria were used as a cutoff in a quality control of the raw data: mean Q score template ≥ 7 and sequence length ≥1000 bp. After quality control, the data were assembled using canu  (Koren et al. , 2017)  and then corrected by Pilon  (Walker et al. , 2014). After removing redundant sequences, circlator  (Hunt et al. , 2015)  was implemented to move the origin of the sequence to the start site of the genome replication to obtain the final genome sequence. The data were aligned to the assembled genome using minimap2  (Li, 2018) , and the sequence depth of each locus was calculated by SAMtools  (Li et al. , 2009). Plasmid sequence was identified by aligning the sequences to a plasmid database  (Jesus et al. , 2019)  using blastn software  (Camacho et al. , 2009) , and coding genes were predicted by prodigal  (Hyatt et al.
, 2010). The tRNA genes were predicted by tRNAscan-SE  (Lowe and Chan, 2016) , rRNA genes were predicted by RNAmmer  (Lagesen et al. , 2007) , and other ncRNAs were predicted by infernal  (Nawrocki and Eddy, 2013)  searching the Rfam database  (Kalvari et al. , 2018). CRISPR was predicted by minced  (Bland et al. , 2007) , and Islander  (Hudson et al. , 2015)  was used for predicting gene islands. Interproscan  (Jones et al. , 2014)  was used to annotate the predicted genes from the TIGRFAMs  (Haft et al. , 2013) , Pfam  (Kalvari et al. , 2018) , and GO  (Ashburner et al. , 2000)  databases. Blastp was used to align the protein sequences to the Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa et al. , 2014)  and RefSeq databases  (O'Leary et al. , 2016). The protein sequences were also aligned to the clusters of orthologous groups (COG) database using rpsblast  (Camacho et al.
, 2009)  for COG annotation. For genome comparison between S. dextrinosolvens Z6 and other related members of the genus, draft genome sequences of four S. dextrinosolvens strains and one Succinimonas amylolytica strain were downloaded from the NCBI database (accession numbers: GCA_900167015. 1, GCA_900116345. 1, GCA_000702045. 1, GCA_900114195. 1, and NZ_KB899636. 1 for strain DSM 3072, ACV-10, H5, 22B, and S. amylolytica DSM_2873 used as an outgroup). Circular comparison plots between these genomes were prepared using BLAST Ring Image Generator (BRIG) (version 0. 95)  (Alikhan et al. , 2011) , and inference of clusters of orthologous groups was performed with OrthoFinder (version 1. 1. 5) (Emms and Kelly, 2015). A total of 1044 gene families exclusively containing single copy genes were selected as potential phylogenetic markers.
For each of these clusters of orthologous groups, protein family alignments were built with muscle (version 3. 8)  (Edgar, 2004). Phylogenetic trees were constructed with RAxML (version 8. 2. 9 SSE3)  (Stamatakis, 2014). To calculate the average nucleotide identity (ANI) between strains, the OAT (Orthologous ANI Tool) software package was used  (Lee et al. , 2016). Venn maps were made using the VennDiagram R package  (Chen and Boutros, 2011).
The libraries were then sequenced using the Illumina HiSeq platform for 2 × 150 bp sequencing. RNA-seq quality control of raw data was performed using the FastQC 1  and HTQC tools  (Yang et al. , 2013). Once the samples had passed the quality assessment, depending on the quality assessment results, the RNA-seq read quality trimming was performed using the sickle software (Version1. 33) (Fass, 2011)  and SeqPrep 2  program. The high-quality sequences were aligned to the complete genome of S. dextrinosolvens Z6 using Burrow-Wheeler Aligner (BWA) and DIAMOND  (Buchfink et al. , 2014). High-throughput functional annotation was performed using Blast2GO (Version 2. 5). The reference genome was analyzed using six major databases (NR, Swiss-Prot, Pfam, COG, GO, and KEGG) to obtain gene function information.
The quantitative expression software RSEM  (Li and Dewey, 2011 ) was used to quantitatively analyze the expression levels of genes and transcripts. Differentially expressed genes (DEGs) between different nitrogen source groups were determined using the R/Bioconductor package, DESeq  (Anders and Huber, 2010)  with read counts calculated by HTseq  (Anders et al. , 2015). Read count normalization was performed using the regularized logarithm (rlog) method in DESeq. DEGs were selected if the adjusted p < 0. 05 and the fold change ≥ 1. 5. The top DEGs related to nitrogen metabolism were selected for this analysis.
Statistical Analysis The enzyme activities (urease, GDH, GS, and GOGAT) and MCP data were analyzed using the mixed model, and real-time PCR data were analyzed by one-way ANOVA in SAS 9. 2 (SAS Institute SAS/STAT(R), 2009). The model included fixed effects of time and nitrogen sources (ammonia, amino acid, and urea), and the results are presented as histograms made using GraphPad Prism version 8. 0. 0 for Windows, GraphPad Software, San Diego, California, United States 3. Least square means are reported, and the significance was set to P < 0. 05. The correlations between the activity of the analyzed enzymes and the expression of the corresponding DEGs were described by using Pearson's correlation coefficient.
Data Availability The complete genome sequence for S. dextrinosolvens strain Z6 has been deposited in GenBank under the accession number CP047056, BioProject number PRJNA545926, and BioSample number SAMN13552832. The raw genome sequence data are available in the Sequence Read Archive (SRA) database under accession number SRR10680263. The raw data for transcriptome sequencing are available in the same Bioproject number, Biosample number SAMN15009434, and in the SRA database under accession number SRR11855358-SRR11855371.
Complete S. dextrinosolvens Z6 Genome Sequence Following enrichment and spreading on plates, a single bacterial colony was isolated based on its morphological growth (red color) on the solid M3 media. PCR amplification using urease gene primers with DNA from the selected colony as a template resulted in a clear band, and extracts derived from cells from the colony had urease activity (85. 4 ± 1. 77 NH 3 nmol/min/mg protein). Furthermore, 16S rRNA sequencing with subsequent comparison to NCBI database sequences using BLAST revealed that the selected colony showed the highest identity to S. dextrinosolvens strain 0554 (98. 13% identity). We named the strain isolated here, S. dextrinosolvens Z6. We determined that the S. dextrinosolvens Z6 genome is 3. 47 Mbp, with a G + C content of 38. 9% (Figure  1A ), a total of 2993 coding sequences, 21 rRNA genes (7 copies of each 5S rRNA, 16S rRNA, and 23S rRNA), 69 tRNAs, 7 ncRNAs, 3 gene islands, and 5 CRISPR repeats (Table  1 ).