dataesr/scientific-paragraphs-categorization huggingface dataset:
The graphical presentation of linkage maps and QTLs was achieved using MAPCHART software version 2. 2  65. QTL analyses were first performed on the measured variables of cracking tolerance. Subsequently, the same type of analysis was conducted after the arcsin (rootsquare) transformation of variables. No large differences were observed and only the results of analyses using transformed variables were presented. For each population and each year, three types of analyses were conducted: (i) by using the transformed observed phenotypic values; (ii) by using predicted values per genotype, provided by the selected model including rainfall-and fruit quality-related covariates; and (iii) by using predicted values per genotype, provided by the selected model including only rainfall-related covariates.
Acknowledgements This work was mainly funded by the  INRAE  sweet cherry breeding program, which is supported by  INRAE BAP division  and by  INRAE 's private partner, CEP Innovation. We thank  Bordeaux INRAE Fruit Tree Experimental Unit (UEA) , for the management of trees as well as for its contribution to phenotyping activities. Phenotyping was made possible thanks to the heavy involvement of numerous students and to several INRAE's technicians. We particularly thank  Jacques Joly ,  Lydie Fouilhaux ,  David Alletru , and  Julien Parmentier. The publication is dedicated to the memory of  Yves Tauzin.
Data availability The datasets generated for this study, concerning all the genetic maps, can be found in the Genome Database for Rosaceae (https://www. rosaceae. org/ publication_datasets). Accession no. tfGDR1019 and tfGDR1037. Concerning the raw phenotypic and climatic data (rainfall), all details can be found in the Excel file 'Raw data' within the supplementary information. Sheets 'RxL', 'RxG',.
However, we have now studied the spatial ecology of one iconic invader-the cane toad, Rhinella marina-in sufficient detail, in enough places, to fulfil this criterion. Native to South America, toads were translocated to Hawai'i in 1932, and from there to northeastern Australia in 1935-in both cases, to control insect pests of commercial agriculture. In Australia, the toads have since greatly expanded their range across the tropics to the west (moving into increasing hot and arid environments) and along the eastern coast to the south (into cooler climates). The westwards expansion across the tropics has been accompanied by a rapid acceleration in rates of expansion of the geographic range of toads  14. In the present paper, we collate and analyse raw data from 44 radio-tracking studies on cane toads in three countries (French Guiana, USA and Australia) to explore general patterns of dispersal-relevant behaviour.
For example, soapberry bugs (Leptocoris tagalicus) likely exhibited at least a partial switch in hostplant preference prior to evolving longer mouthparts that enhanced the bugs' ability to feed on the invasive plant  46  , and lizards (Anolis carolinensis) presumably shifted to higher perches following invasion by competitors before evolving morphological adaptations (larger toepads) that facilitate arboreal activity  47. Analogously, cane toads appear to have evolved more frequent shifts among refuge-sites prior to evolving enhanced rates of daily dispersal. In summary, our data on cane toads provide strong evidence for shifts in dispersal behaviour in the course of a biological invasion. Importantly, we have measured actual rates of dispersal rather than relying upon putative links between an individual's phenotype and its likely rate of dispersal.
The toads failed to control insect pests  55  but dispersed rapidly. Rates of invasion accelerated from around 10 km per annum to > 50 km per annum as the toads spread westward through Queensland and the Northern Territory into Western Australia  14, 38, 56  , but spread rates remained low as the toads moved south into cooler regions of southern Queensland and New South Wales  30, 31, 33. Methods. We compiled original data from 44 radio-tracking studies (total of 29 sites, 650 toads; n = 3-75 toads tracked per site) that we conducted using consistent methods. Adult cane toads were captured in the wild, fitted with small radio-transmitters on waist belts (Holohil model PD-2, 2. 5 gm, < 3% of toad body mass), released at the site of capture, and then relocated on a daily basis (where possible) for at least 5 days thereafter.
e. , was found in different shelters > 5 m apart on successive days). From these two variables we also calculated (3) daily distance moved in cases where the toad changed shelter-sites (i. e. , omitting zeros for times when toads did not change shelter-sites from one day to the next). Ethical note. This paper is based on a collation of data from previous research; no additional animals were handled in the course of the current work. In the original studies, all experimental protocols were approved by institutional committees (University of Sydney Animal Care and Ethics Committee; James Cook University Animal Care and Ethics Committee) and were conducted in accordance with all relevant guidelines and regulations, including the ARRIVE guidelines. Analysis. We classified each study site in terms of time since invasion by toads (based on historical records  14, 60  ).
Both datasets help to better characterize the large spatial and temporal variability in hydrological patterns across the basin, with SWH exhibiting an annual amplitude of more than 5 m in the northern subbasins, while the Congo River main stream and Cuvette Centrale tributaries vary in smaller proportions (1. 5 to 4. 5 m). Furthermore, SWH and SWE help illustrate the spatial distribution and different timings of the CRB annual flood dynamic and how each subbasin and tributary contribute to the.
Ndehedehe et al. (2019) , using the observed Standardized Precipitation Index (SPI) and the global sea surface temperature, examined the impact of the multi-scale ocean-atmosphere phenomena on hydro-climatic extremes, showing that 40 % of the basin during 1994-2006 was affected by severe multi-year droughts. Recently,  Fatras et al. (2021)  analysed the hydrological dynamics of the CRB using inundation extent estimates from the multi-angular and dual polarization passive L-band microwave signals from the Soil Moisture and Ocean Salinity (SMOS) satellite along with precipitation for 2010-2017. The mean flooded area was found to be 2. 39 % for the entire basin, and the dataset helped to characterize floods and droughts during the last 10 years. In addition to remote sensing observations, hydrological modelling represents a valuable tool for studying the CRB water cycle  (Tshimanga et al.
, 2020)  and large-scale modelling  (Paris et al. , 2022). The aim of this study is therefore twofold. First, we provide, for the very first time, an intensive and comprehensive validation of long-term remote-sensing-derived products over the entire CRB, in particular radar altimetry water level variations (a total of 2311 VSs over the period of 1995 to 2020) and surface water extent from multi-satellite techniques from 1992 to 2015 (GIEMS-2;  Prigent et al. , 2020) , using an unprecedented in situ database (28 gauges of river discharge and height) containing the historical and current records of river flows and stages across the CRB. Next, these long-term observations are used to analyse the spatiotemporal dynamics of the water propagation at subbasin-and basin-scale levels, significantly improving our understanding of surface water dynamics in the CRB.
The paper is organized as follows. Section 2 provides a brief description of the CRB. The data and the method employed in this study are described in Sect. 3. Section 4 is dedicated to the validation and evaluation of the satellite surface hydrology datasets, and it presents their main characteristics in the CRB. The results are presented in Sect. 5, and they focus on the use of the satellite datasets to understand the spatiotemporal variability in surface water in the CRB. Finally, the conclusions and perspectives are provided in Sect. 6.
Initiatives, such as Congo HYdrological Cycle Observing System (Congo-HYCOS), have been carried out to build the capacity to collect data and produce consistent and reliable information on the CRB hydrological cycle  (OMM, 2010). For the present study, we have access to a set of historical and contemporary observations of river water stages (WSs) and discharge (Table  1 ). Those were obtained thanks to the collaboration with the regional partners of the Congo Basin Water Resources Research Center (CRREBaC), from the Environmental Observation and Research project (SO-HyBam; https://hybam. obs-mip. fr/fr/, last access: 19 January 2022), and from the Global Runoff Data Centre database (GRDC; https://www. bafg. de/GRDC/ EN/02_srvcs/21_tmsrs/210_prtl/prtl_node. html, last access: 19 January 2022).
While ERS-2, ENV, SRL, and J2 missions are past missions, J3 and S3A/B are still ongoing missions. The VSs used in this study were either directly downloaded from the global operational database of Hydroweb  (http://hydroweb. theia-land. fr, last access: 19 January 2022) or processed manually using MAPS and ALTIS software (respectively, Multi-mission Altimetry Processing Software and Altimetric Time Series Software;  Frappart et al. , 2015a Frappart et al. , , b, 2021b) )  and GDRs (geophysical data records) provided freely by the CTOH (Center for Topographic studies of the Oceans and Hydrosphere; http://ctoh. legos. obs-mip. fr/, last access: 19 January 2022). We thus reached a total number of 323 VSs from ERS-2, 364 and 342 VSs for ENV and ENV2 (new orbit of ENVISAT since late 2010), respectively, 146 and 98 VSs for J2 and J3, respectively, 358 VSs for SRL, 354 VSs for S3A, and 326 VSs for S3B (Fig.
Evaluation of surface water extent characteristics from GIEMS-2 Figure  6  shows the surface water extent (SWE) main patterns over the CRB. Figure  6a  and b display, respectively, the mean and the mean annual maximum in the extent of surface water over the 1992-2015 period. Figure  6c  shows the variability in SWE, expressed in terms of the standard deviation over the period. Figure  6d  provides the average month of SWE annual maximum over the record. The figures show plausible spatial distributions of the major drainage systems, rivers, and tributaries (Lualaba, Congo, Ubangui, and Kasaï) of the CRB. The dataset indeed delineates the main wetlands and inundated areas in the region such as in the Cuvette Centrale region, the Bangweulu swamps, and the valley that contains several lakes (Upemba).
Seasonal water travel time through the rivers and subbasins of the CRB The water travel time through the rivers and subbasins of the CRB was previously investigated by using observations from a few in situ gauges  (Bricquet, 1993). In this study, SWH and SWE datasets enable a similar analysis at the large scale, with an extended analysis to the entire CRB. Here, we determine the maximum of the linear Pearson correlation coefficient by considering a time lag between the satellite-derived SWH at each VS (from ERS-2, ENV, J2/3, SRL, and S3A missions) and GIEMS SWE at each cell, against the Brazzaville/Kinshasa SWH and discharge, respectively. For this, we use the scipy. stats. pearsonr package from Python that also includes the computation of the p value that we use for performing the hypothesis test of the significance of the correlation coefficient.
In the following, we consider the significance level to be 0. 1. Note that the temporal shift between SWH/SWE and in situ stages and discharges is constrained between acceptable values, i. e. it cannot be negative, as we are investigating the time needed by surface waters to reach the Brazzaville/Kinshasa station. For each VS, the longest possible time series is used. For GIEMS-2, the data over the 24-year record are used against the entire river discharge record. The maps of the highest correlations and their corresponding time shifts are provided in Fig. 11. Note that both satellite-derived datasets are jointly analysed to support and complement each other's individual result. As a validation, the linear Pearson correlation coefficients between altimeter-derived SWH and GIEMS-2 SWE for each location within a 25 km distance and a common availability of data were estimated (figure not shown).
Conclusion and perspectives The present study uses a unique joint analysis of in situ and satellite-derived observations to better characterize the CRB surface hydrology and its variability. First, thanks to the availability of an in situ database of historical and contemporary observations of water levels and discharges, we provide an intensive and comprehensive validation of longterm (∼ 25-year) time series from space-borne water level variations and surface water extent throughout the CRB. The comparison of radar-altimetry-derived water levels with the in situ water stage at the interannual scale shows an overall good agreement, with standard errors, in general, lower than 0. 30 m. The analysis of the rmsd across the various missions shows an improvement over time from ERS-2 (tens of centimetres) to S3A/B (a few centimetres) missions, confirming the technological improvement in terms of sensors and data processing.
A total of more than 2300 VSs covering the 1995-2020 period was used in this study and is now freely available. When compared to in situ observations, GIEMS-2 SWE also shows consistent and complementary information at the subbasin and basin scales. These two long-term records are then used to analyse the spatiotemporal dynamics of surface freshwater and its propagation at subbasin and  basin scales, significantly improving our understanding of how surface water flows in the CRB. The analysis of the large database of SWH from altimetry shows that the amplitude varies greatly across the basin, from more than 5 m in the Ubangui and Sangha rivers, while the Cuvette Centrale region and the southern basins display smaller annual variations (1. 5 to 4. 5 m). The maximum level is reached in September-October in the northern part of the basin, in November-December in the central part, and in March-April in the Lualaba region.
Northern subbasins and the central Congo region highly contribute to the large August-March peak, while the southern part supplies water to both peaks and in particular to the second one. These results are supported by in situ observations to confirm the findings from satellite observations and from previous studies. Our results therefore confirm the suitability of both long-term water surface elevation time series from radar altimetry and flooded areas from GIEMS-2 for monitoring the CRB surface water dynamics, potentially bridging the gap between past in situ databases and current and future monitoring as an ensemble. Their use in hydrological models will permit a better representation of local-and basin-scale hydrodynamics and ensure an improved monitoring of hydrological variables from space.
The very first use of a large dataset of VSs spread over more than 100 tributaries across the basin and spanning the whole altimetry period permitted an unprecedented analysis in terms of both the length of the observation and number of observations, providing time series of more than 20 years over the CRB. This unique dataset of surface water levels variations, combined to the ∼ 25-year SWE from GIEMS, should permit us to generate estimates of surface water storage. Complementary to the GRACE/GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) total water storage estimates, it will further permit the estimation of long-term and interannual variations in the freshwater volume in the CRB, including subsurface and groundwater storage and their link with hydro-climatic processes across the region.
Furthermore, the use of both satellite datasets in the hydrological models will permit a better representation of local-and basin-scale hydrodynamics and ensure an improved real-time monitoring of hydrological variables from space, as well as a better evaluation of climate variability impacts on water availability. These datasets will also play a key role in the evaluation and validation of future hydrologyoriented satellite missions, such as the NASA-CNES Surface Water and Ocean Topography (SWOT), which is to be launched in late 2022. More generally, the use of satellitederived observations dedicated to surface hydrology will contribute to a better fundamental understanding of the CRB and its hydro-climatic processes, bringing more opportunities for other river basins in Africa to improve the management of water resources.
Since these datasets are global, our study and the methodology will benefit similar investigations in other ungauged tropical river basins. Data availability. The altimetry data over inland water bodies are distributed via the Hydroweb website (time series of water levels in the rivers and lakes around the world, http://hydroweb. theia-land. fr/; Hydroweb, 2022). The SWH dataset over the Congo is freely available from this online platform. For GIEMS-2, the dataset is available upon request to Catherine Prigent (catherine. prigent@obspm. fr). Author contributions. BK, FP, AP, RTM, and SC conceived the study. BK processed the data and performed the analysis. BK, FP, and AP analysed and interpreted the results and wrote the draft. RTM and CP were responsible for the data curation. All authors discussed the results and contributed to the final version of the paper.
Acknowledgements. Benjamin Kitambo  has been supported by a doctoral grant from the  French Space Agency (CNES) ,  Agence Française du Développement (AFD) , and  Institut de Recherche pour le Développement (IRD). This research has been supported by the  CNES   TOSCA  project " DYnamique hydrologique du BAssin du CoNGO (DYBANGO) " ( 2020-2023 ). Financial support. This research has been supported by the  Centre National d'Etudes Spatiales  (project  TOSCA DYBANGO ). Review statement. This paper was edited by  Shraddhanand Shukla  and reviewed by  Paul Bates  and one anonymous referee.
Bioinformatic Analyses of T47D RNA-seq Data The fastq sequences were trimmed with Cutadapt, quality controlled with FastQC, and mapped to gencode. v32. transcripts. fa. gz (GRCh38 transcriptome) using salmon1. 1 with decoy sequences  [16]. The resulting quant files were then used for exploratory data analysis and to identify differentially-expressed transcripts at the gene level using tximeta  [17] , DESeq2  [18] , csaw  [19] , edgeR  [20] , limma  [21] , and voom  [22]. The salmon quant files and R scripts are available in Zenodo (DOI: 10. 5281/zenodo. 5519652). The fastq files have been deposited at the EBI Array Express Database under accession number E-MTAB-10861.
GTEx, TCGA and CCLE Transcriptome Analyses GTEx v. 8 data were downloaded and analyzed with R code deposited in Zenodo (DOI: 10. 5281/zenodo. 5414141). H2AFJ expression data in human cancer tissues (Cancer Genome Atlas, TCGA) and human cancer cell lines (the Broad Cancer Cell Line Encyclopedia, CCLE) were downloaded from the CBioportal (https://www. cbioportal. org/, last accessed on 18 October 2021) and the Cell minerCDB (https://discover. nci. nih. gov/rsconnect/ cellminercdb/, accessed on 18 October 2021) portals, respectively. Graphs were generated using GraphPad Prism.
Cancer Survival Plots The breast cancer survival plots were generated by the Breast Cancer Gene-Expression Miner  [27]  v4. 7 Web site (http://bcgenex. ico. unicancer. fr/BC-GEM/GEM-Accueil. php? js=1, accessed on 20 July 2021). ). The R script analysis of prostate cancer survival as a function of H2AFJ expression was deposited in Zenodo (DOI: 10. 5281/zenodo. 5517186). The remaining cancer survival data plots were generated by OncoLnc [28].
J expression (Figure  1  and Supplementary Figure  S1 ). In particular, we saw increased levels of H2A. J in luminal epithelial cells of multiple glands, including mammary, prostate, thyroid, pancreas, stomach, bladder, and salivary glands. Similar staining was observed for the corresponding human and mouse tissues. We next mined transcriptome databases to see whether differences in H2A. J protein levels by immunohistochemistry were correlated with differences in H2AFJ RNA levels in different tissues. First, poly A+ RNA-seq data in transcripts per million (TPM) were analyzed for 27 different tissues from the GTEx (Genotype-Tissue Expression) Portal (Figure  2A )  [29]. This subset contained TPM data for 9 to 2642 recently deceased generally non-diseased human donors depending on tissue type (Supplementary Table  S1 ).
These data were not normalized to an external RNA spike-in, and so can only be used as a qualitative indication of relative RNA expression between tissues. We next mined transcriptome databases to see whether differences in H2A. J protein levels by immunohistochemistry were correlated with differences in H2AFJ RNA levels in different tissues. First, poly A+ RNA-seq data in transcripts per million (TPM) were analyzed for 27 different tissues from the GTEx (Genotype-Tissue Expression) Portal (Figure  2A )  [29]. This subset contained TPM data for 9 to 2642 recently deceased generally non-diseased human donors depending on tissue type (Supplementary Table  S1 ). These data were not normalized to an external RNA spike-in, and so can only be used as a qualitative indication of relative RNA expression between tissues.
H2AFZ and H2AFJ also showed the greatest tissue-specific variation in RNA levels (Figure  2B  and Supplementary Figure  S3 ). Whole tissue RNA and protein determinations do not account for cell-type specific expression within tissues. The IHC images show cell-type specific variation within various tissues, such as the strong labeling of luminal epithelial cells. IHC of brain slices showed that whereas most cell types including neurons expressed low levels of H2A. J, a minor cell population expressed H2A. J at much higher levels (unpublished data). We examined single-cell RNA databases for human and mouse tissues to check for cell-type specific variation in H2AFJ expression.
This result is evident as well from a heat map of gene expression comparing H2AFJ expression with that of the top 10 marker genes for each of these clusters (Figure  3B ). The preferential expression of H2afj in luminal mammary cells is conserved in the mouse as seen with an integrated dataset of 50,407 epithelial cells from 5 independent studies leading to the definition of 6 cell clusters: a mammary stem/progenitor cell cluster (MaSc-pro), a basal cell cluster (Basal), a luminal hormone-sensitive cell cluster (L-Hor), a progenitor cluster for the L-Hor cells (LH-pro), a luminal alveolar cell cluster (L-Alv), and a progenitor cluster for the L-Alv cells (LA-pro)  [23]. H2afj expression was higher in all the luminal clusters compared to the MaSc-pro and Basal clusters, with the highest expression in the hormone-sensitive luminal clusters (Figure  3C ).
H2A. J Is a Marker of Luminal Breast and Prostate Cancer Cells H2AFJ was reported to be over-expressed in some cancers  [7] [8] [9] 11] , and interrogation of the Cancer Genome Atlas (TCGA) database showed that prostate and breast cancer expressed high levels of H2AFJ mRNA (Figure  4 , left panel). H2AFJ RNA was also highly expressed in many cancer cell lines derived from glandular tissues such as the breast, pancreas, lung, colorectum, thyroid, esophagus, stomach, and prostate according to the Broad Cancer Cell Line Encyclopedia (CCLE) (Figure  4 , right panel). (C) H2afj gene expression in a UMAP clustering of single cells from the mouse mammary gland. This UMAP analysis revealed 6 cell clusters: Luminal-Hormone-sensitive (L-Hor), Luminal-Hormone-sensitive progenitor (LH-pro), Luminal-Alveolar (L-Alv), Luminal-Alveolar progenitor (LA-pro), Basal, and Mammary Stem/progenitor Cell (MaSc-pro).