dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this procedure did not work for 61 catchments, with a catchment area error exceeding 10 % compared to the available metadata. For these basins, a manual procedure with the Arcmap ® software has been implemented to delineate the catchment boundaries from flow di- rection maps. In addition, for several hundred catchments it was possible to compare the results of the catchment delineation procedure with the catchment areas available in the SIEREM database and the ORSTOM reports (available online at the address: https://horizon. documentation. ird. fr, last access: 13 April 2021), which have been most often individually delineated and carefully checked from ground knowledge over the years  (Boyer et al. , 2006). From the delineated catchments, the mean, maximum, and minimum altitudes from the HydroSHEDS DEM have been extracted and included in the metadata.
In addition, the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) data (ESA, 2017) have been extracted for each catchment for the year 2015. This database contains land cover maps at a 300 m spatial resolution for 38 classes, compliant with the UN Land Cover Classification System (LCCS). The classes have been grouped into eight new classes: forest, urban areas, cropland, irrigated croplands, grassland, shrubland, sparse vegetation, and bare land. Overall, the basins are characterized by a low proportion of urban areas, a large proportion of forests, especially in the intertropical zone (mean = 41 %, median = 37 %), and a majority of non-irrigated cultivated areas, on average covering 31 % of the total area of the basins. Indeed, the irrigated crops represent only 0. 43 % on average.
River regulation Dams and reservoirs have also been extracted and added in the metadata of the stations. The Global Reservoir and Dam Database (GRanD) v1. 3  (Lehner et al. , 2011)  has also been considered to identify regulated basins. The number of dams included in each river basin has been extracted using the catchment boundaries. As shown in the metadata of GRanD, most of the dams in African basins were constructed around the 1970s (Fig. 4 ). The rivers could be considered regulated if at least one dam exists in the catchment area, otherwise the river is considered natural (Fig. 5 ). However, the influence of dams and reservoirs on the flow regime are linked to the location of the regulation structure, the portion of the basin controlled, and the management strategies. For instance, in a large basin with only one dam located on a small headwater catchment, its influence may not be distinguishable at the river outlet.
On the other hand, a station located immediately downstream of a dam outlet may have its flow regime strongly impacted by dam operations. It should also be noted that other regulation structures like small dams or water diversion channels that may not be included in GRanD could be present in the catchments considered natural  (Lehner et al. , 2011; Pekel et al. , 2016). This is particularly the case Basins are considered regulated if they contain at least one dam or reservoir from GRanD  (Lehner et al. , 2011). Mean annual precipitation between 1970 and 2000 is provided from the WorldClim database  (Fick and Hijmans, 2017). in semi-arid areas where earthen-made channels, often informal, draw their water supply from the river itself through the building of small diverting structures  (Underhill, 1984; Kimmage, 1991).
Hydrometric indices Here is presented the list of indices computed from daily discharge data. While hydrological indices refer to standard statistical metrics, such as the mean, maximum, or percentiles computed from time series of discharge data, hydrological signatures can be defined as metrics describing the hydrological behavior and the dominant processes in a river basin  (Addor et al. , 2018). Most of the indices are computed with the Toolbox for Streamflow Signatures in Hydrology (TOSSH; available at the address: https://github. com/TOSSHtoolbox/, last access: 13 April 2021)  (Gnann et al. , 2021). The indices and signatures selected span a large variety of runoff characteristics from high to low flows from previous literature  (Poff et al. , 1997; Richter et al. , 1996; Baker et al.
, 2004; Yadav et al. , 2007; Clark et al. , 2009; Estrany et al. , 2010; Sawicz et Y. Tramblay et al. : ADHI:  The African Database of Hydrometric Indices  (1950 -2018 ) al. , 2011; Euser et al. , 2013; Safeeq et al. , 2013; Addor et al. , 2018; McMillan, 2020).
Indices computed on monthly or annual basis These indices have been computed for each calendar year for consistency with other databases such as GSIM  (Do et al. , 2018; Gudmundsson et al. , 2018). These indices have been computed for the years with less than 5 % missing data: 1. mean annual runoff 2. minimum of 7 d consecutive streamflow per year and corresponding date 3. annual maximum runoff and the corresponding date 4. annual values for the 5th, 10th, 25th, 50th 75th, 90th, 95th, and 99th percentiles of daily streamflow. In addition to these annual series, the monthly time series contains for each month the mean, maximum, and minimum runoff, the last column being the number of missing days per month. There is one file per station. It is advised to consider the monthly values only for the months with no missing values or missing values less than 10 % or 5 %.
Data availability The ADHI is available for download at: https://doi. org/10. 23708/LXGXQ9 (Tramblay and Rouché, 2020). Different files are supplied in the ADHI database. The ADHI_stations. tab file contains the station metadata (Table  2 ), and the ADHI_summary. tab file contains for each station the variables described in Table  3. The compressed folders AnnualMean. zip, Annual-Max. zip, Annual7DayMin. zip, and AnnualPercentiles. zip contain time series for mean annual runoff, annual maximum runoff, annual minimum of 7 d discharge, and annual values for the 5th, 10th, 25th, 50th 75th, 90th, 95th, and 99th percentiles of daily streamflow. There is one file per station. The data files for the annual mean, maximum, and 7 d minimum contain as columns the year, month, day, and the data as the last column.
The data files for the percentiles contain the year as the first column, and then the other columns contain the percentile values. The compressed folder MonthlySeries. zip contains as columns (after the year and month) the mean, maximum, and minimum monthly runoff, and the last column is the number of missing days per month. There is one file per station. The compressed folder Plots. zip contains for each station a plot of the daily discharge data available. The compressed folder Catchment_boundaries. zip contains the catchment boundaries in the shapefile format (one. shp file per basin). The compressed folder Catchment_plots. zip contains for each basin a plot of the catchment area in PNG format. Additional indices could be computed upon reasonable request to the corresponding author.
Conclusions and perspectives This new hydrological database brings together the largest number of African river flow measurement stations in comparison with other previously published datasets. In this ADHI dataset, we included a total of 1466 stations with at least 10 years of discharge data between 1950 and 2018 for a mean record length of 33. 3 years. Half of the stations have more than 30 years of data. By comparison, the recent GSIM database contains 979 stations in Africa, with a record length varying from 1 to 110 years until 2015 and a mean record length of 33. 8 years. This ADHI results from a pooling of the GRDC and SIEREM databases, built from contributions of several agencies in African countries in charge of the management of hydrological measurement networks. This database will be regularly updated with data from SIEREM and GRDC.
Since most of the preprocessing steps have been automated, it would be possible to increase the number of stations considered or the length of the data series if more data would become available. The data from the SIEREM database are already regularly updated from contributions of different institutes. In the future, individual contributions from researchers or institutes will be also welcome to increase the spatiotemporal coverage of the data. The FRIEND program (UNESCO/IHP) will also contribute to increase the number of stations through coordinated efforts at the regional level. The dataset provides a series of indices that describe a wide range of mean and extreme runoff properties, allowing for the characterization of the hydrological regime and applications linked to the management of water resources and https://doi.
org/10. 5194/essd-13-1547-2021 Earth Syst. Sci. Data, 13, 1547-1560, 2021 Frequency of zero-flow days hydrological risks. This database includes different catchment sizes and rivers with different hydrological regimes that make it possible to analyze the behavior of rivers in very different contexts for a wide range of scales. More broadly, this ADHI could contribute to a better knowledge of African hydrology. For instance, the impacts of dams on river discharge remain largely unquantified at the scale of Africa  (Biemans et al. , 2011). From these indices, various applications can be sought. For example, the percentiles of the daily streamflow could be useful to calibrate hydrological models using the flow duration curve  (McMillan et al. , 2017)  and to constrain model outputs  (Tumbo and Hughes, 2015; Ndzabandzaba and Hughes, 2017).
Flow duration curves are also useful for catchment classification according to their rainfall-runoff response  (Cheng et al. , 2012). In the recent years, global runoff simulations have been provided by the Global Flow Awareness System, with land surface or global hydrological models driven by reanalysis data  (Alfieri et al. , 2020; Harrigan et al. , 2020). Yet, due to the small number of stations representing African basins in the currently available databases preventing a robust calibration of the models, the hydrological simulations have a poor performance  (Harrigan et al. , 2020). More generally, this new ADHI could open perspectives to apply hydrological models in African basins, in particular combined with recent remote sensing data products  (Brocca et al. , 2019; Satgé et al. , 2020).
Beside deterministic hydrological modeling approaches, several statistical methods to estimate the return levels of floods have been proposed in order to safely design dams, reservoirs, sewers, or other water regulation structures. Regional frequency analysis methods have been applied to estimate floods in ungauged basins in several African countries such as Morocco  (Zkhiri et al. , 2017) , Tunisia  (Ellouze and Abida, 2008) , South Africa  (Nathanael et al. , 2018; Smakhtin et al. , 1997) , and the Volta basin  (Komi et al. , 2016). However, studies at a larger regional scale remain very scarce  (Farquharson et al. , 1992; Padi et al. , 2011)  while there is a strong need to improve the knowledge of hydrological hazards in African countries  (Di Baldassarre et al. , 2010). With this recent database becoming available, it could be possible to develop regional frequency analysis techniques for floods or low flows tailored for the African context, taking also into account the impacts of global changes.
Acknowledgements. River runoff has been obtained from the  Global Runoff Data Centre, 56068 Koblenz, Germany , and included in the database with their authorization. We would like to thank the  GRDC  (https://www. bafg. de/GRDC/,last access:  13 April 2021 ) for granting access to their data. A large part of the data processed in the present study comes from the SIEREM database (http://www. hydrosciences. fr/sierem,last access:  13 April 2021 ), and authors wish to express their gratitude to all the persons who contributed to this database over the years. The authors wish to thank  Sebastian Gnann  for his assistance with and feedback on the TOSSH toolbox (https://github. com/TOSSHtoolbox/,last access:  13 April 2021 ). This work is dedicated to the memory of  Claudine Dieulin  who passed away in January 2020 during the course of this project.
Elucidation of biosynthetic pathways producing metabolites with sporadic distribution across plant phylogeny reveals multiple examples of convergent evolution where CYPs have been independently recruited from the same or different CYP families, to adapt to similar environmental challenges or ecological niches. Sometimes only a single or a few mutations are required for functional interconversion. A compilation of functionally characterized plant CYPs is provided online through the Plant P450 Database (erda. dk/public/vgrid/PlantP450/).
Lineage-specific gene proliferation of paralogs is often termed ''blooms''  (Feyereisen, 2011). A CYP family bloom is, for example, found in O. sativa with 90 full-length CYP71s (88 CYP71 plus two CYP99 subfamily genes), some members of which are reported to be involved in diterpenoid phytoalexin biosynthesis (e. g. ,  Wang et al. , 2011; Wu et al. , 2011; Zhan et al. , 2020). Another example is the fern Cystopteris utahensis, where 44 different CYP981 transcripts were identified from 1kP transcriptomes  (One Thousand Plant Transcriptomes, 2019) , although none of them has been assigned a function. The number of functionally characterized CYPs is steadily increasing, and more than 800 plant CYPs have been characterized by their ability to metabolize one or more substrates. These are collected in The Plant Cytochrome P450 Database (erda.
2% of protein-coding genes in some algal species. The chlorophyte Volvox carteri has 19 CYP genes, Trentepohlia annulata has 58, and most chlorophytes fall in between these sizes, like C. reinhardtii with 39 CYP genes. In the partially annotated genome of the charophyte Penium margaritaceum, 46 CYP genes have been recorded so far. In green algae, CYPs are highly diversified. Green algae is the plant group featuring the highest number of CYP families, the majority of which are algae specific (Figure  1 , supplemental data 3). To date, the number of algal CYP families has not shown signs of saturation and there are probably many more to be discovered. Only 41 CYP families are shared between chlorophyte and charophyte algae (about 12%), showing the significant divide between the two groups. Fungal CYP55 (nitric oxide synthase) is also present in chlorophytes, but not in charophytes, which, together with high sequence identity, might indicate lateral transfer of CYP55 after the two algal clades split.
Amino acid sequences (954) were aligned using Clustal Omega and phylogeny was inferred using the neighbor-joining method. A high-resolution phylogenetic tree with tip labels is available in supplemental Figure  1. and CYP710. A couple of CYP families appeared in green algae and are conserved in some land plants but were lost during evolution (supplemental data 1). For example, the CYP746 family (clan 746) is present in algae, moss, liverworts, and a few ferns, but not in hornworts or seed plants (spermatophytes). This is the only example of a CYP clan that is present in early-diverging plant lineages and lost upon evolution of vascular plants. The CYP765 family (clan 72) is conserved from green algae to gymnosperms and lost in angiosperms. Mining of the 1kP transcriptomes and genomes of early land plants revealed many taxaspecific families (Figure  1 ; supplemental data 1 and 2), some of which show great abundance, such as the fern-specific CYP981 family (clan 71), which is the richest CYP family in this taxon  (Thodberg et al.
, 2014). The plant CYP74 family has previously been dated back to liverworts  (Nelson and Werck-Reichhart, 2011) , but investigation of more algae and early land plant datasets shows presence of CYP74 members in hornworts, charophytes, and freshwater chlorophytes. CYP74 genes have also been identified in some animal species, such as cnidarians. CYP74s may thus predate the split between animals and plants or the cnidarian copies must be a result of lateral transfer. The rarity of CYP74 outside the plants favors a lateral transfer otherwise many gene losses would be required. Members of the plant CYP74 family are involved in the metabolism of oxylipins, including jasmonic acid, which functions in cell signaling and defense  (Wasternack and Feussner, 2018; Farmer and Goossens, 2019). They are atypical CYPs, not requiring electron donation and molecular oxygen, and use fatty acid hydroperoxides as substrates.
More studies into the substrate space of individual CYPs may provide essential knowledge to understand the evolution-specific functional characteristics. Knowledge of enzyme promiscuity may also guide engineering of desired specificities for, e. g. , optimized biotechnological applications, and may help to predict and manage metabolism of pesticides, other encountered exogenous chemicals, and pollutants. We hope that The Plant Cytochrome P450 Database will facilitate search for hitherto functionally characterized CYPs for further investigations, biotechnological applications, and the discovery of CYPs with unknown functions.
ACKNOWLEDGMENTS We thank  Drs. Charles Francis Delwiche  and  Endymion Cooper  for early access to green algal transcriptomes supported by  NSF , grant  DEB-1036506. We also thank  Dr. Elizabeth H. J. Neilson  for comments on early versions of the manuscript,  Dr. Francois Andre  ´for providing the protein model structures in Figure 5, and  Jakob Bo Cetti Hansen  for help coding The Plant Cytochrome P450 Database. The authors declare no.
Neutralizer. Inoculated devices were held for 2 h at room temperature (20 6 2 C). After each combination's required hold time 1 mL buffer was spread-plated onto MOX agar and incubated for 24 h at 35 C and the colonies were counted and recorded. After counting, samples were decoded, and the mean and standard deviation for each combination was calculated. An ANOVA was carried out to determine if the means were significantly different between the combinations separately for each target strain. Data demonstrated that small changes in testing parameters did not impact the performance of the sampling device. The study parameters, data summary, and ANOVA results for each target analyte and treatment combination are presented in Tables  12 13 14 15 16.
Cyclic Voltammetry Measurements. A DLK-SUB IV electrochemical analyzer from Analytical Instrument Systems, Inc. was used to collect O 2 and H 2 S data  (22). Briefly, this instrument contained a potentiostat, a multiplexer (to monitor up to four solid-state Au amalgam [Au/Hg] working electrodes one at a time), and a computer, which communicated with another computer in the Alvin. The electronics were enclosed in a titanium housing and powered via a cable from Alvin. A Delrin wand housed the Au/Hg-working electrodes, which were cabled to the analyzer, and 1/8-inch Teflon tubing, which allowed sampling via the syringe sampler. The wand was positioned by a manipulator from Alvin; the solid-state Ag/AgCl reference and Pt counter electrodes were placed on the Alvin basket at 1. 6 °C in order to keep their temperature constant.
The wand was positioned by a manipulator from Alvin. For each discrete location sampled by syringe, 5 to 7 voltammetric scans were taken and the data averaged. The potential range scanned was -0. 1 to -1. 8 V to -0. 1 V at a scan rate of 2,000 mV • s -1. A 5-s conditioning step at -0. 9 V and another at -0. 1 V for 2 s was applied before each scan to electrochemically clean the Hg surface. The minimum detectable limits for free sulfide (Σ [H 2 S] and [HS -]) and O 2 are 0. 2 and 3 μM, respectively. Lastly, the in situ voltammetry system did not detect thiosulfate (MDL, 10 μM) or polysulfides (MDL, 0. 2 μM). Electrodes were calibrated before and after each Alvin dive to ensure data reliability. Fe and Mn Speciation Measurements. Six samples noted in Table  1  were processed for soluble Fe and Mn speciation following collection using the syringe system described above without any preloaded reagents or buffer.
Using the known reaction rate constant "k 1 " for the reaction with Mn(II) in a sample completely reduced with hydroxylamine, "k 2 ," the rate constant for the Mn(III)-L ligand substitution reaction, could be obtained and, subsequently, the concentration of each Mn species was determined in the sample. The differential kinetics of the reaction of Mn(II) and weak Mn(III)-L complexes with the added porphyrin were observed over 15 min using a quartz 1-cm cell in an ultraviolet-visible diode array spectrophotometer coupled with Olis Globalworks software, but weak Mn(III)-L complexes (MDL is 50 nM) were not detected. Samples were also amended with hydroxylamine to assess the presence of strong Mn(III)-L complexes (not outcompeted by added porphyrin), and only strong Mn(III)-L complexes were detected (MDL is 50 nM).
ACKNOWLEDGMENTS. This work was supported by the  Department of Chemistry and Biochemistry at University of South Carolina , the  School of Marine Science & Policy at University of Delaware , and the  NSF  through  CO OCE-1559274  and  CHE-1308801  to T. J. S. and  CO OCE-1558738  and  MGG OCE-1558712  to G. W. L. III. Kathryn Shaw  completed the artwork for Fig. 1. We thank the  Alvin group , which is part of the  National Deep Submergence Facility at Woods Hole Oceanographic Institution , and the captain and crew of the research vessel Atlantis for their support in all aspects of our research. We also thank the guest editor and two anonymous reviewers for comments and suggestions that have improved this manuscript.
Methods: We analyzed the presence of HSC markers in the human fetal kidneys by analysis of single-cell datasets. We then analyzed in kidney organoids derived from induced pluripotent stem cells (iPSCs) the presence of hematopoietic markers using transcriptome analyses. Results: Twelve clusters were identified as stromal, endothelial, and nephron cell typespecific markers in the two fetal stage (17 weeks) kidney datasets. Among these, the expression of hematopoietic cells in cluster 9 showed an expression of primitive markers. Moreover, whole transcriptome analysis of our iPSC-derived kidney organoids revealed induction of the primitive hematopoietic transcription factor RUNX1 as found in the human fetal kidney cortex.
, 1996). To our knowledge, there has been no study analyzing the possibility of donor-derived hematopoiesis after kidney transplantation. It should be reminded that in the majority of cases, kidney transplants are performed using kidneys from deceased donors  (Stachura et al. , 2009; Butler et al. , 2018). HSC-kidney niche interactions have been studied in many reports. For example, zebrafish kidney stromal cell lines can support and maintain early hematopoietic precursors and differentiation of lymphoid, myeloid, and erythroid precursors  (Stachura et al. , 2009). Recent advances in single-cell RNA sequencing technology are leading to new discoveries and validation in fetal organs and organoids  (Butler et al. , 2018; Menon et al. , 2018; Park et al. , 2018). Here, we first analyzed the presence of transcriptional markers of HSCs in fetal kidneys through analysis of a single-cell dataset described by  Butler et al.
Human Fetal Kidney Cortex Harbors Cells Expressing Hematopoietic Transcripts Single-cell transcriptome is a powerful technology to investigate cell heterogeneity in a tissue. Butler et al. (2018) ,  Lindström et al. (2018) ,  Menon et al. (2018), and Park et al. (2018)  performed these experiments in cortex tissues isolated from two human fetal kidneys (17 weeks) by 10× genomics technology. This work allowed us to perform in silico analyses. To this end, we merged and analyzed with Seurat package the 2 respective MTX files generated by cell ranger in order to suppress batch error and to perform downstream unsupervised analysis. To build the common matrix of the two samples, genes that were found expressed in a minimum of five cells by sample were conserved. After the data from the two kidney samples were merged, the Seurat digital matrix comprised 7,860 cells for 18,119 transcripts.